From 8866557c1f651445f6a51de43687010149c06ac1 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Tue, 15 Oct 2024 16:50:55 +0100
Subject: [PATCH 01/87] temp for moving laptops

---
 .../profiling-ml-on-arm/Streamline.png        | Bin 0 -> 128971 bytes
 .../profiling-ml-on-arm/_index.md             |  39 ++++
 .../profiling-ml-on-arm/_next-steps.md        |  27 +++
 .../profiling-ml-on-arm/_review.md            |  45 ++++
 .../app-profiling-android-studio.md           |  13 ++
 .../app-profiling-streamline.md               | 200 ++++++++++++++++++
 .../profiling-ml-on-arm/example-picture.png   | Bin 0 -> 63167 bytes
 .../nn-profiling-executenetwork.md            |  13 ++
 .../nn-profiling-general.md                   |  13 ++
 .../profiling-ml-on-arm/plan.txt              |  20 ++
 .../profiling-ml-on-arm/why-profile.md        |  23 ++
 11 files changed, 393 insertions(+)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png
new file mode 100644
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
new file mode 100644
index 0000000000..2bd02eb5c9
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -0,0 +1,39 @@
+---
+title: How to Profile ML on Arm
+
+minutes_to_complete: 30
+
+who_is_this_for: ML App developers who want to see the performance of their models on Arm devices.
+
+learning_objectives: 
+    - Profile timings of ML models on Arm devices.
+    - Profile ML application performance on Arm devices.
+
+prerequisites:
+    - Some application development.
+    - Arm device with Android OS.
+
+author_primary: Ben Clark
+
+### Tags
+skilllevels: Intermediate
+subjects: PLACEHOLDER SUBJECT
+armips:
+    - Cortex-X
+    - Cortex-A
+    - Mali
+    - Immortalis
+tools_software_languages:
+    - Android Studio
+    - tflite
+operatingsystems:
+    - Android
+    - Linux
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
new file mode 100644
index 0000000000..c4ae77cc92
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -0,0 +1,27 @@
+---
+next_step_guidance: PLACEHOLDER TEXT 1
+
+recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+
+further_reading:
+    - resource:
+        title: PLACEHOLDER MANUAL 
+        link: PLACEHOLDER MANUAL LINK
+        type: documentation
+    - resource:
+        title: PLACEHOLDER BLOG 
+        link: PLACEHOLDER BLOG LINK
+        type: blog
+    - resource:
+        title: PLACEHOLDER GENERAL WEBSITE 
+        link: PLACEHOLDER GENERAL WEBSITE LINK
+        type: website
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 21                  # set to always be larger than the content in this path, and one more than 'review'
+title: "Next Steps"         # Always the same
+layout: "learningpathall"   # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
new file mode 100644
index 0000000000..9b1bfb96fe
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -0,0 +1,45 @@
+---
+review:
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 1?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+        correct_answer: 1                    
+        explanation: >
+            PLACEHOLDER EXPLANATION 1
+
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 2?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 4                   
+        explanation: >
+            PLACEHOLDER EXPLANATION 2
+               
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 3?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 2          
+        explanation: >
+            PLACEHOLDER EXPLANATION 3
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+title: "Review"                 # Always the same title
+weight: 20                      # Set to always be larger than the content in this path
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
new file mode 100644
index 0000000000..1d63e0df09
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -0,0 +1,13 @@
+---
+title: App Profiling with Android Studio
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
new file mode 100644
index 0000000000..65ccd6eb74
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -0,0 +1,200 @@
+---
+title: App Profiling with Streamline
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Application Profiling
+Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
+
+The profiler can look at memory, CPU activity and cycles, cache misses, and may parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
+
+![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
+
+## Streamline
+Streamline (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and the data is then analyzed on the host machine.
+
+There are many <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">tutorials and training videos</a> on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use <a href="https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application">these instructions for Linux</a> setup and capture.
+
+For now, as per these <a href="https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en">setup instructions</a>, make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools <a href="https://developer.android.com/studio/releases/platform-tools.html">here</a>.
+
+Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the platform-tools directory to your path. The instructions for this will depend on your host machine's operating system.
+
+Next, install <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads">Arm Performance Studio</a>, which includes Streamline. 
+
+Connect your device to your computer through USB. Ensure that your device is set to Developer mode.
+
+On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
+
+Next, you need a debuggable build of the application you want to profile. 
+- In Android Studio, ensure your *Build Variant* is set to `debug`. You can then build the application and install it on your device. 
+- For a Unity app, select Development Build under File > Build Settings when building your application. 
+- In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
+- In the general case, you can set `android:debuggable=true` in the application manifest file.
+
+You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but we intend to add additional useful information first.
+
+## Custom Annotations
+
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. In the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+
+To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from <a href="https://github.com/ARM-software/gator/tree/main/annotate">here</a>. Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+
+These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is skightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this <a href="https://developer.android.com/studio/projects/add-native-code">here</a>.
+
+For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+
+```c
+#include <jni.h>
+#include "streamline_annotate.h"
+
+JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateSetup(JNIEnv* env, jobject obj) {
+    gator_annotate_setup();
+}
+
+JNIEXPORT jlong JNICALL Java_AnnotateStreamline_GetTime(JNIEnv* env, jobject obj) {
+    return gator_get_time();
+}
+```
+
+Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion:
+
+```c
+JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* env, jobject obj, jint color, jstring str) {
+    const char* nativeStr = (*env)->GetStringUTFChars(env, str, 0);
+    gator_annotate_marker_color(color, nativeStr);
+    (*env)->ReleaseStringUTFChars(env, str, nativeStr);
+}
+```
+
+In Android Studio `cmake` is used to create your C library, so we need a CMakelists.txt file in the same directory as the C file. This will look like:
+
+```cmake
+# Sets the minimum CMake version required for this project.
+cmake_minimum_required(VERSION 3.22.1)
+
+# Declare the project name. 
+project("StreamlineAnnotationJNI")
+
+# Create and name the library
+add_library(${CMAKE_PROJECT_NAME} SHARED
+    annotate_jni_wrapper.c
+    streamline_annotate.c)
+
+# Specifies libraries CMake should link to your target library. 
+# Adding in the Android system log library pulls in the NDK path.
+find_library( # Sets the path to the NDK library.
+        log-lib
+        log )
+
+target_link_libraries( # Specifies the target library.
+        ${CMAKE_PROJECT_NAME}
+        ${log-lib} )
+```
+
+If you add the following to your `build.gradle` file, you will be able to call the functions from your Kotlin code:
+
+```gradle
+    externalNativeBuild {
+        cmake {
+            path file('src/main/cpp/CMakeLists.txt')
+            version '3.22.1'
+        }
+    }
+```
+
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here we create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of our code:
+
+```kotlin
+// Kotlin wrapper class for integration into Android project
+class AnnotateStreamline {
+    init {
+    // Load the native library
+        System.loadLibrary("StreamlineAnnotationJNI")
+    }
+
+    companion object {
+    // #defines for colors from the Streamline Annotation c code
+        const val ANNOTATE_RED: UInt = 0x0000ff1bu
+        const val ANNOTATE_BLUE: UInt = 0xff00001bu
+        const val ANNOTATE_GREEN: UInt = 0x00ff001bu
+        const val ANNOTATE_PURPLE: UInt = 0xff00ff1bu
+        const val ANNOTATE_YELLOW: UInt = 0x00ffff1bu
+        // any other constants you want from the included gator files
+
+        // Create an instance of the AnnotateStreamline class
+        private val annotations = AnnotateStreamline()
+
+        // Function to setup the Streamline Annotation - call this first
+        @JvmStatic
+        fun setup() {
+            annotations.AnnotateSetup()
+        }
+
+        // Function to get the current time from gator
+        @JvmStatic
+        fun getTime(): Long {
+            return annotations.GetTime()
+        }
+
+        // more functions that you want, e.g. (note UInt conversion)
+        @JvmStatic
+        fun annotateMarkerColorStr(color: UInt, str: String) {
+            annotations.AnnotateMarkerColorStr(color.toInt(), str)
+        }
+        // ...
+    }
+
+    // externals match the last part of function names in annotate_jni_wrapper.c
+    external fun AnnotateSetup()
+    external fun GetTime(): Long
+    external fun AnnotateMarkerColorStr(color: Int, str: String)
+    // ...
+}
+```
+
+The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
+
+```kotlin
+      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Important event")
+```
+
+## Custom Activity Maps (CAMs)
+
+In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown above. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
+
+To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+
+Here is an example setup in a class's companion object.
+
+```kotlin
+  companion object {
+    const val camViewId = 1u
+    const val trackRoot = 1u
+    const val trackChild = 2u
+    baseId = (0u..UInt.MAX_VALUE/2u-5000u).random()
+    currentId = baseId
+
+    init {
+      AnnotateStreamline.camViewName(camViewId, "Inference")
+      AnnotateStreamline.camTrack(camViewId, trackRoot,0xffffffffu, "Root") // root wants -1 for parent id
+      AnnotateStreamline.camTrack(camViewId, trackChild, trackRoot, "Children")
+    }
+```
+
+Then they can be used like this:
+
+```kotlin
+      val preprocess = currentId++
+      AnnotateStreamline.camJobStart(camViewId, preprocess, "Preprocess", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_YELLOW)
+      val childjob = currentId++
+      AnnotateStreamline.camJobStart(camViewId, childjob, "child job", trackChild, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_CYAN)
+      //child job code...
+      AnnotateStreamline.camJobEnd(camViewId, childjob, AnnotateStreamline.getTime())
+      //rest of preprocessing code...
+      AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
+```
+
+Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. This will make it obvious whether the time spent in your application is on the inference, ML pre-processing, ML post-processing, or other parts of your application.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
new file mode 100644
index 0000000000..551fc2959e
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -0,0 +1,13 @@
+---
+title: ML profiling with ExecuteNetwork
+weight: 6
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
new file mode 100644
index 0000000000..ea589f59a2
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -0,0 +1,13 @@
+---
+title: Profiling the Neural Network
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
new file mode 100644
index 0000000000..70e7667178
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
@@ -0,0 +1,20 @@
+
+want the performance of your ML app
+memory and compute
+
+how can you find that out
+
+different steps:
+- ML network
+- app around the ML network, especially pre and post processing, and the network as a whole
+
+for around the ML network - streamline profiler
+here's how to do that...
+Also Android Profiler, memory example
+
+Ml network, it will depend on the inference engine you are using
+- here's an example for if you are using ArmNN with TFLite
+- if you're not using it, it may still have some useful information, but different operators will be used and their performance will be different
+can see structure with netron or google model explorer to compare operators or different versions of networks
+may need to use a conversion tool to convert to TFLite (or whatever your inference engine wants)
+
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
new file mode 100644
index 0000000000..3864750f69
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -0,0 +1,23 @@
+---
+title: What do you need to profile?
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Performance
+Working out what is taking the time and memory in your application is the first step to getting the performance you want. Profiling can help you identify the bottlenecks in your application and understand how to optimize it.
+
+With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
+
+We will be looking at an Android example using TFLite in this learning path, but most of the tools also work with Linux to cover a wide range of Arm devices. The principles are the same for other inference engines and platforms, but the tools are different.
+
+## Tools
+
+Currently different tools are needed if you want to look inside the NN inference, or at the application's performance as a whole.
+
+For profiling the ML inference we will look at <a href="https://github.com/ARM-software/armnn/releases">ArmNN</a>'s ExecuteNetwork.
+
+For profiling the application as a whole we will look at <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">Arm Performance Studio</a>'s Streamline, and at the Android Studio Profiler.
+

From a593790d21d0b6c379ba26ddd45671b5a6a4d6ee Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Mon, 21 Oct 2024 17:15:15 +0100
Subject: [PATCH 02/87] network profiling and ExecuteNetwork pages

---
 .../profiling-ml-on-arm/_index.md             |   2 +-
 .../app-profiling-android-studio.md           |   6 +-
 .../profiling-ml-on-arm/example-picture.png   | Bin 63167 -> 0 bytes
 .../nn-profiling-executenetwork.md            |  68 ++++++++++++++++--
 .../nn-profiling-general.md                   |  11 +--
 5 files changed, 74 insertions(+), 13 deletions(-)
 delete mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 2bd02eb5c9..a57070750e 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -17,7 +17,7 @@ author_primary: Ben Clark
 
 ### Tags
 skilllevels: Intermediate
-subjects: PLACEHOLDER SUBJECT
+subjects: Machine Learning
 armips:
     - Cortex-X
     - Cortex-A
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 1d63e0df09..e2873f0af1 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -6,8 +6,6 @@ weight: 4
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## Android Memory Profiling
+Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index 551fc2959e..aeafa6bc16 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -6,8 +6,68 @@ weight: 6
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## ArmNN's Network Profiler
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, there is a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
+If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite, but not ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+
+To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
+
+To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
+
+From a command prompt, you can run the following commands to push the files to your phone:
+
+```bash
+adb push model.tflite /data/local/tmp/
+adb push ExecuteNetwork /data/local/tmp/
+adb push libarm_compute.so /data/local/tmp/
+adb push libarmnn.so /data/local/tmp/
+adb push libarmnn_support_library.so /data/local/tmp/
+```
+Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version on of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+
+Then you need to set the permissions on the files:
+
+```bash
+adb shell        # to get into the adb shell on the phone
+cd /data/local/tmp
+chmod 777 ExecuteNetwork    # to make the file executable
+chmod 777 *.so	   # to make the library files executable
+```
+
+Then you can run the model with the following command:
+
+```bash
+LD_LIBRARY_PATH=. ./ExecuteNetwork -m model.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
+```
+
+This will run the model twice, outputting the layer timings to `modelout.txt`. Specifically, the `-e` and `--output-network-details` flags will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. The `--iterations 2` flag will run the model twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+
+After running the model, you can pull the output file back to your computer with the following commands:
+
+```bash
+exit         # to leave the adb shell
+adb pull /data/local/tmp/modelout.txt
+```
+Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
+
+Depending on the size of your model, the output will probably be quite large. You can use a text editor or a tool like `less` to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
+
+At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
+```text
+Info: ArmNN v33.2.0
+Info: Initialization time: 7.20 ms.
+Info: ArmnnSubgraph creation
+Info: Parse nodes to ArmNN time: 50.99 ms
+Info: Optimize ArmnnSubgraph time: 85.94 ms
+Info: Load ArmnnSubgraph time: 91.11 ms
+Info: Overall ArmnnSubgraph creation time: 228.47 ms
+
+Info: Execution time: 721.91 ms.
+Info: Inference time: 722.02 ms
+
+Info: Execution time: 468.42 ms.
+Info: Inference time: 468.58 ms
+```
+
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations & their timings recorded before the network itself, with the layers' "Wall clock time" in microseconds of how long they took to run. These layers and their timings can then be analysed to see which layers, and which operators, took the most time.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index ea589f59a2..da4fe08de0 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -6,8 +6,11 @@ weight: 5
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## Profiling your model
+App profilers will give you a good overall view of your performance, but often you might want to look inside the model and work out bottlenecks within the network. The network is often the bulk of the time, in which case it will warrant closer analysis. 
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
+General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
+
+Depending on your model the tools available will differ. For example, if you are using TensorFlow Lite, Arm provides the ArmNN delegate that you can run the model with on Linux or Android. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+
+If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.

From 3e4ad22a25afae7ad077fb229a0766dddba3debd Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Sun, 27 Oct 2024 18:08:00 +0000
Subject: [PATCH 03/87] done, pending final read-through

---
 .../profiling-ml-on-arm/_next-steps.md        |  18 +++------
 .../profiling-ml-on-arm/_review.md            |  38 +++++++++---------
 .../android-profiling-version.png             | Bin 0 -> 18717 bytes
 .../app-profiling-android-studio.md           |  34 ++++++++++++++++
 .../app-profiling-streamline.md               |  20 ++++-----
 .../profiler-jk-allocations-legend.png        | Bin 0 -> 32173 bytes
 .../start-profile-dropdown.png                | Bin 0 -> 6980 bytes
 .../profiling-ml-on-arm/why-profile.md        |   6 +--
 8 files changed, 72 insertions(+), 44 deletions(-)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/start-profile-dropdown.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
index c4ae77cc92..aafea4126a 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -1,21 +1,15 @@
 ---
-next_step_guidance: PLACEHOLDER TEXT 1
+next_step_guidance: Continue Learning
 
-recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+recommended_path: /learning-paths/smartphones-and-mobile/build-llama3-chat-android-app-using-executorch-and-xnnpack/
+recommended_path: /learning-paths/smartphones-and-mobile/kleidiai-on-android-with-mediapipe-and-xnnpack/
 
 further_reading:
     - resource:
-        title: PLACEHOLDER MANUAL 
-        link: PLACEHOLDER MANUAL LINK
+        title: Arm Streamline User Guide  
+        link: https://developer.arm.com/documentation/101816/latest/
         type: documentation
-    - resource:
-        title: PLACEHOLDER BLOG 
-        link: PLACEHOLDER BLOG LINK
-        type: blog
-    - resource:
-        title: PLACEHOLDER GENERAL WEBSITE 
-        link: PLACEHOLDER GENERAL WEBSITE LINK
-        type: website
+
 
 
 # ================================================================================
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index 9b1bfb96fe..814fe6c32d 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -2,37 +2,37 @@
 review:
     - questions:
         question: >
-            PLACEHOLDER QUESTION 1?
+            Streamline Profiling lets you profile:
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-        correct_answer: 1                    
+            - Arm CPU activity
+            - Arm GPU activity
+            - when your Neural Network is running
+            - All of the above
+        correct_answer: 4                    
         explanation: >
-            PLACEHOLDER EXPLANATION 1
+            Streamline will show you CPU and GPU activity (and a lot more counters!), and if Custom Activity Maps are used, you can see when your Neural Network and other parts of your application are running.
 
     - questions:
         question: >
-            PLACEHOLDER QUESTION 2?
+            Does Android Studio have a profiler?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 4                   
+            - Yes
+            - No
+        correct_answer: 1                   
         explanation: >
-            PLACEHOLDER EXPLANATION 2
+            Yes, Android Studio has a built-in profiler that can be used to monitor the memory usage of your app among other things
                
     - questions:
         question: >
-            PLACEHOLDER QUESTION 3?
+            Is there a way to profile what is happening inside your Neural Network?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 2          
+            - Yes, Streamline just shows you out of the box
+            - No
+            - Yes, ArmNN's ExecuteNetwork can do this
+            - Yes, Android Studio Profiler can do this
+        correct_answer: 3          
         explanation: >
-            PLACEHOLDER EXPLANATION 3
+            Standard profilers don't have an easy way to see what is happening inside an ML framework to see a model running inside it. ArmNN's ExecuteNetwork can do this for TensorFlow Lite models, and ExecuTorch has tools that can do this for PyTorch models.
 
 
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index e2873f0af1..03ba77f991 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -9,3 +9,37 @@ layout: learningpathall
 ## Android Memory Profiling
 Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
+To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
+
+Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if you're using LiteRT (formerly TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
+
+Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
+
+![Android Studio profiling run types alt-text#center](android-profiling-version.png "Figure 1. Profiling run versions")
+
+For the Java/Kotlin side, you want the **debuggable** "Profile 'app' with complete data", which is based off the debug variant. For the native side, you want the **profileable** "Profile 'app' with low overhead", which is based off the release variant.
+
+### Java/Kotlin
+
+If we start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then we can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
+
+![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
+
+When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown in below in Figure 3.
+![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
+
+The default height of the Profiling view is usually too small, so that will need adjusting to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+
+Looking further down you can see the Table of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
+
+### Native
+
+For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you need to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
+
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the Table and Visualization tabs. The Table tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose callstack and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+
+In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
+
+## Other platforms
+
+On other platforms, you will need a different memory profiler. The objective of working out where the memory is being used is the same, and whether there are issues with leaks or just too much memory being used. There are often trade-offs between memory and speed, and they can be considered more sensibly if the numbers involved are known.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 65ccd6eb74..af5a13a975 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -9,22 +9,22 @@ layout: learningpathall
 ## Application Profiling
 Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
 
-The profiler can look at memory, CPU activity and cycles, cache misses, and may parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
+The profiler can look at memory, CPU activity and cycles, cache misses, and many parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
 ## Streamline
-Streamline (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and the data is then analyzed on the host machine.
+[Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and then analyzed on the host machine.
 
-There are many <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">tutorials and training videos</a> on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use <a href="https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application">these instructions for Linux</a> setup and capture.
+There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
 
-For now, as per these <a href="https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en">setup instructions</a>, make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools <a href="https://developer.android.com/studio/releases/platform-tools.html">here</a>.
+For now, as per these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the platform-tools directory to your path. The instructions for this will depend on your host machine's operating system.
+Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
 
-Next, install <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads">Arm Performance Studio</a>, which includes Streamline. 
+Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
-Connect your device to your computer through USB. Ensure that your device is set to Developer mode.
+Connect your Android phone to your computer through USB. Ensure that your device is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
 
 On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
 
@@ -38,11 +38,11 @@ You can now run Streamline, and do a capture of your application. This can be us
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. In the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from <a href="https://github.com/ARM-software/gator/tree/main/annotate">here</a>. Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
 
-These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is skightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this <a href="https://developer.android.com/studio/projects/add-native-code">here</a>.
+These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
 For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 3864750f69..8a0d20d1a6 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -15,9 +15,9 @@ We will be looking at an Android example using TFLite in this learning path, but
 
 ## Tools
 
-Currently different tools are needed if you want to look inside the NN inference, or at the application's performance as a whole.
+Currently, you need to use different tools to profile the ML inference or the application's performance as a whole.
 
-For profiling the ML inference we will look at <a href="https://github.com/ARM-software/armnn/releases">ArmNN</a>'s ExecuteNetwork.
+For profiling the ML inference we will look at [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole we will look at <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">Arm Performance Studio</a>'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole we will look at [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
 

From faac583d06636047bcab940010d635951dc95dc8 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Mon, 28 Oct 2024 13:47:35 +0000
Subject: [PATCH 04/87] editing on self-review

---
 .../profiling-ml-on-arm/_index.md               |   2 +-
 .../profiling-ml-on-arm/_review.md              |   2 +-
 .../app-profiling-android-studio.md             |  16 ++++++++--------
 .../app-profiling-streamline.md                 |  16 ++++++++++------
 .../nn-profiling-executenetwork.md              |  15 ++++++++-------
 .../profiling-ml-on-arm/nn-profiling-general.md |   2 +-
 .../profiling-ml-on-arm/streamline_marker.png   | Bin 0 -> 52790 bytes
 7 files changed, 29 insertions(+), 24 deletions(-)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index a57070750e..593c7e1116 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -3,7 +3,7 @@ title: How to Profile ML on Arm
 
 minutes_to_complete: 30
 
-who_is_this_for: ML App developers who want to see the performance of their models on Arm devices.
+who_is_this_for: Machine Learning App developers who want to see the performance of their models on Arm devices.
 
 learning_objectives: 
     - Profile timings of ML models on Arm devices.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index 814fe6c32d..a1f46d1ed9 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -27,7 +27,7 @@ review:
             Is there a way to profile what is happening inside your Neural Network?
         answers:
             - Yes, Streamline just shows you out of the box
-            - No
+            - No.
             - Yes, ArmNN's ExecuteNetwork can do this
             - Yes, Android Studio Profiler can do this
         correct_answer: 3          
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 03ba77f991..7d49a7303b 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -1,5 +1,5 @@
 ---
-title: App Profiling with Android Studio
+title: Memory Profiling with Android Studio
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -9,9 +9,9 @@ layout: learningpathall
 ## Android Memory Profiling
 Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
-To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
+To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device in Developer Mode with a USB cable, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
 
-Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if you're using LiteRT (formerly TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
+Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if, for example, you're using LiteRT (formerly known as TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
 
 Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
 
@@ -25,18 +25,18 @@ If we start looking at the [Java/Kotlin side](https://developer.android.com/stud
 
 ![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
 
-When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown in below in Figure 3.
+When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
 ![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
 
-The default height of the Profiling view is usually too small, so that will need adjusting to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+The default height of the Profiling view - as well as the timeline graph within it - is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
 
-Looking further down you can see the Table of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
+Looking further down you can see the *Table* of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
 
 ### Native
 
-For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you need to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
+For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you have to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
 
-The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the Table and Visualization tabs. The Table tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose callstack and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
 
 In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index af5a13a975..976a3d0b7b 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -38,13 +38,13 @@ You can now run Streamline, and do a capture of your application. This can be us
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
 To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
 
 These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+For us, we create a file that we will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under our project. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -94,7 +94,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to your `build.gradle` file, you will be able to call the functions from your Kotlin code:
+If you add the following to the `build.gradle` file of the Module you wish to profile, you will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -158,12 +158,16 @@ class AnnotateStreamline {
 The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
 
 ```kotlin
-      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Important event")
+      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Model Load")
 ```
 
+This colored marker with a string will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline:
+
+![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
+
 ## Custom Activity Maps (CAMs)
 
-In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown above. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
+In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
 To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
 
@@ -197,4 +201,4 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. This will make it obvious whether the time spent in your application is on the inference, ML pre-processing, ML post-processing, or other parts of your application.
+Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index aeafa6bc16..1254741906 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -7,24 +7,25 @@ layout: learningpathall
 ---
 
 ## ArmNN's Network Profiler
-One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, there is a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite, but not ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
 
 To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
 To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
 
-From a command prompt, you can run the following commands to push the files to your phone:
+Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push model.tflite /data/local/tmp/
+adb push model.tflite /data/local/tmp/   # the tflite NN model file that you wish to profile
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
 adb push libarmnn_support_library.so /data/local/tmp/
+# more ArmNN .so library files
 ```
-Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version on of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
 
 Then you need to set the permissions on the files:
 
@@ -51,7 +52,7 @@ adb pull /data/local/tmp/modelout.txt
 ```
 Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
 
-Depending on the size of your model, the output will probably be quite large. You can use a text editor or a tool like `less` to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
+Depending on the size of your model, the output will probably be quite large. You can use a text editor to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
 
 At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
 ```text
@@ -70,4 +71,4 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations & their timings recorded before the network itself, with the layers' "Wall clock time" in microseconds of how long they took to run. These layers and their timings can then be analysed to see which layers, and which operators, took the most time.
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index da4fe08de0..7ca228759f 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -11,6 +11,6 @@ App profilers will give you a good overall view of your performance, but often y
 
 General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
 
-Depending on your model the tools available will differ. For example, if you are using TensorFlow Lite, Arm provides the ArmNN delegate that you can run the model with on Linux or Android. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+Depending on your model the tools available will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run the model with on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
 
 If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png
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From 9e39ed14c54ed89eaca8d8a07636c489e2e61417 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 1 Nov 2024 10:24:04 +0000
Subject: [PATCH 05/87] added information to index

---
 .../microcontrollers/yolo-on-himax/_index.md  |  36 ++++++++++++++
 .../yolo-on-himax/_next-steps.md              |  27 +++++++++++
 .../microcontrollers/yolo-on-himax/_review.md |  45 ++++++++++++++++++
 .../yolo-on-himax/example-picture.png         | Bin 0 -> 63167 bytes
 .../yolo-on-himax/how-to-1.md                 |  10 ++++
 .../yolo-on-himax/how-to-2.md                 |  13 +++++
 6 files changed, 131 insertions(+)
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/_index.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/_review.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
new file mode 100644
index 0000000000..cb9ffb0e50
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -0,0 +1,36 @@
+---
+title: PLACEHOLDER TITLE
+
+minutes_to_complete: 10
+
+who_is_this_for: PLACEHOLDER SENTENCE
+
+learning_objectives: 
+    - PLACEHOLDER OBJECTIVE 1
+    - PLACEHOLDER OBJECTIVE 2
+
+prerequisites:
+    - PLACEHOLDER PREREQ 1
+    - PLACEHOLDER PREREQ 2
+
+author_primary: PLACEHOLDER NAME
+
+### Tags
+skilllevels: PLACEHOLDER SKILLLEVEL
+subjects: PLACEHOLDER SUBJECT
+armips:
+    - PLACEHOLDER IP A
+    - PLACEHOLDER IP B
+tools_software_languages:
+    - PLACEHOLDER TOOL OR SOFTWARE C
+    - PLACEHOLDER TOOL OR SOFTWARE D
+operatingsystems:
+    - PLACEHOLDER OS G
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md b/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
new file mode 100644
index 0000000000..c4ae77cc92
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
@@ -0,0 +1,27 @@
+---
+next_step_guidance: PLACEHOLDER TEXT 1
+
+recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+
+further_reading:
+    - resource:
+        title: PLACEHOLDER MANUAL 
+        link: PLACEHOLDER MANUAL LINK
+        type: documentation
+    - resource:
+        title: PLACEHOLDER BLOG 
+        link: PLACEHOLDER BLOG LINK
+        type: blog
+    - resource:
+        title: PLACEHOLDER GENERAL WEBSITE 
+        link: PLACEHOLDER GENERAL WEBSITE LINK
+        type: website
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 21                  # set to always be larger than the content in this path, and one more than 'review'
+title: "Next Steps"         # Always the same
+layout: "learningpathall"   # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
new file mode 100644
index 0000000000..9b1bfb96fe
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
@@ -0,0 +1,45 @@
+---
+review:
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 1?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+        correct_answer: 1                    
+        explanation: >
+            PLACEHOLDER EXPLANATION 1
+
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 2?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 4                   
+        explanation: >
+            PLACEHOLDER EXPLANATION 2
+               
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 3?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 2          
+        explanation: >
+            PLACEHOLDER EXPLANATION 3
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+title: "Review"                 # Always the same title
+weight: 20                      # Set to always be larger than the content in this path
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png b/content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png
new file mode 100644
index 0000000000000000000000000000000000000000..c69844bed44b65c7f5bc6cf511f93987fdcd7b95
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diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
new file mode 100644
index 0000000000..8b23cb6bf1
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -0,0 +1,10 @@
+---
+title: PLACEHOLDER STEP TITLE 1
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER 
+YOUR CONTENT GOES HERE
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
new file mode 100644
index 0000000000..f884b5592b
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -0,0 +1,13 @@
+---
+title: PLACEHOLDER STEP TITLE 2
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")

From cbe0c9209aaf1660a17283105cf51ae8eb68a9ab Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 1 Nov 2024 10:24:42 +0000
Subject: [PATCH 06/87] added information to index

---
 .../microcontrollers/yolo-on-himax/_index.md  | 34 +++++++++++--------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index cb9ffb0e50..32c51c405b 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -1,31 +1,35 @@
 ---
-title: PLACEHOLDER TITLE
+title: Run Yolo computer vision model on a Himax microcontroller
 
-minutes_to_complete: 10
+minutes_to_complete: 90
 
-who_is_this_for: PLACEHOLDER SENTENCE
+who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U85.
 
 learning_objectives: 
-    - PLACEHOLDER OBJECTIVE 1
-    - PLACEHOLDER OBJECTIVE 2
+    - Run Yolo model on off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55
+    - Build Himax SDK and generate firmware image file
+    - Update firmware on edge device (Himax WiseEye2)
+    
 
 prerequisites:
-    - PLACEHOLDER PREREQ 1
-    - PLACEHOLDER PREREQ 2
+    - [Seeed Grove Vision AI V2 Module](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html)
+    - [OV5647-62 Camera module and included FPC cable](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html)
+    - An Windows based machine running an x86 archiecture. 
 
-author_primary: PLACEHOLDER NAME
+author_primary: Chaodong Gong
 
 ### Tags
-skilllevels: PLACEHOLDER SKILLLEVEL
-subjects: PLACEHOLDER SUBJECT
+skilllevels: Beginner
+subjects: Object Detection
 armips:
-    - PLACEHOLDER IP A
-    - PLACEHOLDER IP B
+    - Cortex M55
+    - Ethos U55
 tools_software_languages:
-    - PLACEHOLDER TOOL OR SOFTWARE C
-    - PLACEHOLDER TOOL OR SOFTWARE D
+    - Himax SDK
+    - bash scripting
 operatingsystems:
-    - PLACEHOLDER OS G
+    - Windows
+    - Linux
 
 
 ### FIXED, DO NOT MODIFY

From 245afa7c6f55125be21aaafe575ebc0d1adfd102 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 1 Nov 2024 11:21:54 +0000
Subject: [PATCH 07/87] copied information from draft pdf

---
 .../microcontrollers/yolo-on-himax/_index.md  |   7 +--
 .../yolo-on-himax/example-picture.png         | Bin 63167 -> 0 bytes
 .../yolo-on-himax/himax_web_ui.jpg            | Bin 0 -> 27405 bytes
 .../yolo-on-himax/how-to-1.md                 |  56 +++++++++++++++++-
 .../yolo-on-himax/how-to-2.md                 |  45 ++++++++++++--
 .../yolo-on-himax/how-to-3.md                 |  33 +++++++++++
 .../yolo-on-himax/how-to-4.md                 |  28 +++++++++
 7 files changed, 157 insertions(+), 12 deletions(-)
 delete mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/himax_web_ui.jpg
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 32c51c405b..f9c722c3ff 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -10,13 +10,12 @@ learning_objectives:
     - Build Himax SDK and generate firmware image file
     - Update firmware on edge device (Himax WiseEye2)
     
-
 prerequisites:
-    - [Seeed Grove Vision AI V2 Module](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html)
-    - [OV5647-62 Camera module and included FPC cable](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html)
+    - Seeed Grove Vision AI V2 Module
+    - OV5647-62 Camera module and included FPC cable
     - An Windows based machine running an x86 archiecture. 
 
-author_primary: Chaodong Gong
+author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
 ### Tags
 skilllevels: Beginner
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png b/content/learning-paths/microcontrollers/yolo-on-himax/example-picture.png
deleted file mode 100644
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diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index 8b23cb6bf1..21ccca5c92 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -1,10 +1,60 @@
 ---
-title: PLACEHOLDER STEP TITLE 1
+title: Set Up Environment
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER 
-YOUR CONTENT GOES HERE
+## Set up the development environment
+
+### Step 2.1. Install Ubuntu 22.04
+
+Himax SDK needs to be compiled in a Linux environment. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. 
+
+### Step 2.2. (Optional) Install Microsoft VS Code
+
+This is only optional. You can use any text editor to view/change code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
+
+### Step 2.3. Install python 3
+
+Go to website python.org to download and install.
+Verify python is installed by
+python3 --version
+You should see an output like the following.
+```bash
+Python 3.12.7
+```
+### Step 2.4. Install python-pip
+
+```bash
+sudo apt update
+sudo apt install python3-pip
+pip3 --version
+```
+
+If `pip3` is correctly installed you should see an output similar to tht following.
+
+```output
+pip 24.2 from <path to pip3>/pip (python 3.12)
+```
+
+### Step 2.5. Install make
+
+You will need to install the make build tool in order to build the firmware in the following section.
+
+```bash
+sudo apt update
+sudo apt install make -y
+```
+
+### Step 2.6. Install ARM GNU toolchain
+```bash
+cd ~
+wget https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
+tar -xvf arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
+export PATH="$HOME/arm-gnu-toolchain-13.2.Rel1-x86_64-arm-none-eabi/bin/:$PATH"
+```
+
+Please note: you may want to add the command to your `bashrc` file. This enables the Arm GNU toolchain to be easily accessed from any new terminal session. 
+
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index f884b5592b..a07524ab24 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -1,13 +1,48 @@
 ---
-title: PLACEHOLDER STEP TITLE 2
+title: Build The Firmware
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## Build The Firmware
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
+Next, we need to build an image that contains the embedded software (firmware). You will need to have the git version control system installed. Run the command below to verify that git is installed on your system.
+
+```bash
+git --version
+```
+
+You should see output similar to that below.
+
+```output
+git version 2.39.3
+```
+
+If not, please follow the steps to install git on your system.
+
+### Step 3.1. Clone the project
+
+You will first need to recusively clone the Himax repository. This will also clone the necessary sub repos such as Arm CMSIS. 
+
+```bash
+git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git
+cd Seeed_Grove_Vision_AI_Module_V2
+```
+
+### Step 3.2. Compile the firmware
+
+```bash
+cd EPII_CM55M_APP_S
+make clean
+make
+```
+
+### Step 3.3. Generate firmware image file
+
+```bash
+cd ../we2_image_gen_local/
+cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
+./we2_local_image_gen project_case1_blp_wlcsp.json
+```
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
new file mode 100644
index 0000000000..1fd66134e5
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -0,0 +1,33 @@
+---
+title: Flash Firmware
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Update The Firmware
+
+Update firmware in Windows environment by python code.
+
+### Step 4.1. Open ‘cmd’ over Seeed_Grove_Vision_AI_Module_V2-main.
+
+### Step 4.2. Install xmodem.
+
+Run the following command 
+
+```python
+pip install -r xmodem/requirements.txt
+```
+
+### Step 4.3. Connect the module to PC by USB cable.
+
+### Step 4.4. Flash the firmware by the following commands:
+
+```python
+python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img 
+```
+
+    Note: For each project, the command might be slightly different. See the instructions on project pages for this command.
+
+After the firmware image burning is completed, the message "Do you want to end file transmission and reboot system? (y)" is displayed. Press the reset button on module to restart.
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
new file mode 100644
index 0000000000..6d88cad5a2
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -0,0 +1,28 @@
+---
+title: Run and View Model Results
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+
+### Step 5.1. Close the terminal session.
+
+### Step 5.2. Connect module to PC with USB cable.
+
+You will need to insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector and slide the connector in with the metal pins facing down.
+
+### Step 5.3. Download the Himax AI web toolkit.  
+
+The Himax AI web toolkit enables a browser-based graphical user interface (GUI) where the live camera feed can be displayed. 
+
+Download the Himax AI Web toolkit by clicking on this [link](https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip)
+
+Unzip the archived file and double click `index.html.`. This will open the GUI within your default browser.
+
+### Step 5.4. Connect to the Grove Vision AI
+
+Select 'Grove Vision AI(V2)` in the top-right hand corner and press connect button. 
+
+![Himax web UI](./himax_web_ui.jpg)

From b9dd8460a23551b05fa2bda9e34802815d337b74 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 1 Nov 2024 14:07:46 +0000
Subject: [PATCH 08/87] copied and validated steps from draft pdf

---
 .../microcontrollers/yolo-on-himax/_index.md  |   3 +-
 .../yolo-on-himax/how-to-1.md                 |  34 +++++++++++++++---
 .../yolo-on-himax/how-to-2.md                 |  12 +++++++
 .../yolo-on-himax/how-to-3.md                 |  25 +++++++++----
 .../yolo-on-himax/how-to-4.md                 |   2 +-
 .../yolo-on-himax/how-to-5.md                 |  30 ++++++++++++++++
 .../yolo-on-himax/latched.jpg                 | Bin 0 -> 39516 bytes
 .../yolo-on-himax/unlatched.jpg               | Bin 0 -> 52263 bytes
 8 files changed, 93 insertions(+), 13 deletions(-)
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/latched.jpg
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/unlatched.jpg

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index f9c722c3ff..a053eef311 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -13,7 +13,8 @@ learning_objectives:
 prerequisites:
     - Seeed Grove Vision AI V2 Module
     - OV5647-62 Camera module and included FPC cable
-    - An Windows based machine running an x86 archiecture. 
+    - A USB-C cable
+    - An Linux-based PC on an x86 archiecture. 
 
 author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index 21ccca5c92..c2f0b496a0 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -8,9 +8,22 @@ layout: learningpathall
 
 ## Set up the development environment
 
-### Step 2.1. Install Ubuntu 22.04
+### Step 2.1. Install Ubuntu
 
-Himax SDK needs to be compiled in a Linux environment. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. 
+Himax SDK needs to be compiled in a Linux environment. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
+
+```bash
+cat /etc/*release*
+```
+The top lines from the terminal output will show the distribution version. 
+
+```output
+DISTRIB_ID=Ubuntu
+DISTRIB_RELEASE=22.04
+DISTRIB_CODENAME=jammy
+DISTRIB_DESCRIPTION="Ubuntu 22.04.5 LTS"
+...
+```
 
 ### Step 2.2. (Optional) Install Microsoft VS Code
 
@@ -22,14 +35,14 @@ Go to website python.org to download and install.
 Verify python is installed by
 python3 --version
 You should see an output like the following.
-```bash
+```output
 Python 3.12.7
 ```
 ### Step 2.4. Install python-pip
 
 ```bash
 sudo apt update
-sudo apt install python3-pip
+sudo apt install python3-pip -y
 pip3 --version
 ```
 
@@ -48,7 +61,20 @@ sudo apt update
 sudo apt install make -y
 ```
 
+Successful installation of make will show the following when the `make --version` command is run.
+
+```output
+$ make --version
+GNU Make 4.3
+Built for x86_64-pc-linux-gnu
+Copyright (C) 1988-2020 Free Software Foundation, Inc.
+License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
+This is free software: you are free to change and redistribute it.
+There is NO WARRANTY, to the extent permitted by law.
+```
+
 ### Step 2.6. Install ARM GNU toolchain
+
 ```bash
 cd ~
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index a07524ab24..3d9dd57298 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -33,12 +33,15 @@ cd Seeed_Grove_Vision_AI_Module_V2
 
 ### Step 3.2. Compile the firmware
 
+The make build tool is used to compile the source code. This should take up around 2-3 minutes depending on the number of CPU cores available.
+
 ```bash
 cd EPII_CM55M_APP_S
 make clean
 make
 ```
 
+
 ### Step 3.3. Generate firmware image file
 
 ```bash
@@ -46,3 +49,12 @@ cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ./we2_local_image_gen project_case1_blp_wlcsp.json
 ```
+
+Your terminal output should end with the following.
+
+```output
+Output image: output_case1_sec_wlcsp/output.img
+Output image: output_case1_sec_wlcsp/output.img
+
+IMAGE GEN DONE
+```
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index 1fd66134e5..bbf1d2b72e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -6,21 +6,32 @@ weight: 3
 layout: learningpathall
 ---
 
-## Update The Firmware
+## Flash The Firmware
 
-Update firmware in Windows environment by python code.
+### Step 4.1. Install xmodem.
 
-### Step 4.1. Open ‘cmd’ over Seeed_Grove_Vision_AI_Module_V2-main.
-
-### Step 4.2. Install xmodem.
+Change the directory
+```bash
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2 # If you cloned the repo to a different location replace $HOME with the path. 
+```
 
-Run the following command 
+`Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. 
 
 ```python
 pip install -r xmodem/requirements.txt
 ```
 
-### Step 4.3. Connect the module to PC by USB cable.
+### Step 4.2. Connect the module to PC by USB cable.
+
+You will need to insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
+
+![unlatched](./unlatched.jpg)
+
+Then, slide the FPC connector in with the metal pins facing down and close the dark grey latch to fasten the connector. 
+
+![latched](./latched.jpg)
+
+Then connect the Groove Vision AI V2 Module to your computer via USB-C.
 
 ### Step 4.4. Flash the firmware by the following commands:
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index 6d88cad5a2..b059ed6948 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -11,7 +11,7 @@ layout: learningpathall
 
 ### Step 5.2. Connect module to PC with USB cable.
 
-You will need to insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector and slide the connector in with the metal pins facing down.
+
 
 ### Step 5.3. Download the Himax AI web toolkit.  
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
new file mode 100644
index 0000000000..c303ada772
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
@@ -0,0 +1,30 @@
+---
+title: (Optional) Try Different Models
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+
+### Modify the makefile
+
+Change the directory to the where the makefile is located.
+
+```bash
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/ # replace $HOME with the location of the project
+```
+
+Using a text editor, for example visual studio code or nano, modify the `APP_TYPE` field in the makefile from the default value of `allon_sensor_tflm` to one of the following. 
+
+
+|Parameter|Description|
+|---|---|
+|tflm_folov8_od|Object detection|
+|tflm_folov8_pose|Pose detection|
+|tflm_fd_fm|Face detection|
+
+Go back to the 'Build The Firmware' section and start from Step 3.2. to regenerate the firmware image. 
+
+The images below are examples images from the model. 
+
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/latched.jpg b/content/learning-paths/microcontrollers/yolo-on-himax/latched.jpg
new file mode 100644
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HcmV?d00001


From d517c7c2c62f31143d16f7b917434a91d8f979f6 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 1 Nov 2024 14:25:51 +0000
Subject: [PATCH 09/87] tidied up formatting

---
 .../microcontrollers/yolo-on-himax/_index.md    |   3 +--
 .../yolo-on-himax/face_detection.jpg            | Bin 0 -> 28018 bytes
 .../microcontrollers/yolo-on-himax/how-to-1.md  |  14 +++++++-------
 .../microcontrollers/yolo-on-himax/how-to-2.md  |   6 +++---
 .../microcontrollers/yolo-on-himax/how-to-3.md  |  13 +++++--------
 .../microcontrollers/yolo-on-himax/how-to-4.md  |  10 ++++------
 .../microcontrollers/yolo-on-himax/how-to-5.md  |  15 +++++++++++++--
 .../yolo-on-himax/object_detection.jpg          | Bin 0 -> 17517 bytes
 .../yolo-on-himax/pose_estimation.jpg           | Bin 0 -> 31719 bytes
 9 files changed, 33 insertions(+), 28 deletions(-)
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/face_detection.jpg
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/object_detection.jpg
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/pose_estimation.jpg

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index a053eef311..21e6533696 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -20,7 +20,7 @@ author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
 ### Tags
 skilllevels: Beginner
-subjects: Object Detection
+subjects: ML
 armips:
     - Cortex M55
     - Ethos U55
@@ -28,7 +28,6 @@ tools_software_languages:
     - Himax SDK
     - bash scripting
 operatingsystems:
-    - Windows
     - Linux
 
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/face_detection.jpg b/content/learning-paths/microcontrollers/yolo-on-himax/face_detection.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ed70940f335e0b96a954cb6178bf3498094b4eb2
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diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index c2f0b496a0..287372e6a0 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -8,9 +8,9 @@ layout: learningpathall
 
 ## Set up the development environment
 
-### Step 2.1. Install Ubuntu
+### Step 1.1. Install Ubuntu
 
-Himax SDK needs to be compiled in a Linux environment. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
+This learning path has been tested on Ubuntu. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
 
 ```bash
 cat /etc/*release*
@@ -25,11 +25,11 @@ DISTRIB_DESCRIPTION="Ubuntu 22.04.5 LTS"
 ...
 ```
 
-### Step 2.2. (Optional) Install Microsoft VS Code
+### Step 1.2. (Optional) Install Microsoft VS Code
 
 This is only optional. You can use any text editor to view/change code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
 
-### Step 2.3. Install python 3
+### Step 1.3. Install python 3
 
 Go to website python.org to download and install.
 Verify python is installed by
@@ -38,7 +38,7 @@ You should see an output like the following.
 ```output
 Python 3.12.7
 ```
-### Step 2.4. Install python-pip
+### Step 1.4. Install python-pip
 
 ```bash
 sudo apt update
@@ -52,7 +52,7 @@ If `pip3` is correctly installed you should see an output similar to tht followi
 pip 24.2 from <path to pip3>/pip (python 3.12)
 ```
 
-### Step 2.5. Install make
+### Step 1.5. Install make
 
 You will need to install the make build tool in order to build the firmware in the following section.
 
@@ -73,7 +73,7 @@ This is free software: you are free to change and redistribute it.
 There is NO WARRANTY, to the extent permitted by law.
 ```
 
-### Step 2.6. Install ARM GNU toolchain
+### Step 1.6. Install ARM GNU toolchain
 
 ```bash
 cd ~
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index 3d9dd57298..7b21f37c45 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -22,7 +22,7 @@ git version 2.39.3
 
 If not, please follow the steps to install git on your system.
 
-### Step 3.1. Clone the project
+### Step 2.1. Clone the Himax project
 
 You will first need to recusively clone the Himax repository. This will also clone the necessary sub repos such as Arm CMSIS. 
 
@@ -31,7 +31,7 @@ git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_
 cd Seeed_Grove_Vision_AI_Module_V2
 ```
 
-### Step 3.2. Compile the firmware
+### Step 2.2. Compile the firmware
 
 The make build tool is used to compile the source code. This should take up around 2-3 minutes depending on the number of CPU cores available.
 
@@ -42,7 +42,7 @@ make
 ```
 
 
-### Step 3.3. Generate firmware image file
+### Step 2.3. Generate firmware image file
 
 ```bash
 cd ../we2_image_gen_local/
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index bbf1d2b72e..6a3713ba84 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -8,20 +8,17 @@ layout: learningpathall
 
 ## Flash The Firmware
 
-### Step 4.1. Install xmodem.
+### Step 3.1. Install xmodem.
 
-Change the directory
-```bash
-cd $HOME/Seeed_Grove_Vision_AI_Module_V2 # If you cloned the repo to a different location replace $HOME with the path. 
-```
 
 `Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. 
 
-```python
+```bash
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2 # If you cloned the repo to a different location replace $HOME with the path. 
 pip install -r xmodem/requirements.txt
 ```
 
-### Step 4.2. Connect the module to PC by USB cable.
+### Step 3.2. Connect the module to PC by USB cable.
 
 You will need to insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
 
@@ -33,7 +30,7 @@ Then, slide the FPC connector in with the metal pins facing down and close the d
 
 Then connect the Groove Vision AI V2 Module to your computer via USB-C.
 
-### Step 4.4. Flash the firmware by the following commands:
+### Step 3.4. Flash the firmware by the following commands:
 
 ```python
 python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index b059ed6948..c0fc075bb3 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -7,13 +7,11 @@ layout: learningpathall
 ---
 
 
-### Step 5.1. Close the terminal session.
+### Step 4.1. Connect module to PC with USB cable.
 
-### Step 5.2. Connect module to PC with USB cable.
+Exit the terminal session and connect the module to the PC via your USB-C cable. 
 
-
-
-### Step 5.3. Download the Himax AI web toolkit.  
+### Step 4.2. Download the Himax AI web toolkit.  
 
 The Himax AI web toolkit enables a browser-based graphical user interface (GUI) where the live camera feed can be displayed. 
 
@@ -21,7 +19,7 @@ Download the Himax AI Web toolkit by clicking on this [link](https://github.com/
 
 Unzip the archived file and double click `index.html.`. This will open the GUI within your default browser.
 
-### Step 5.4. Connect to the Grove Vision AI
+### Step 4.3. Connect to the Grove Vision AI
 
 Select 'Grove Vision AI(V2)` in the top-right hand corner and press connect button. 
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
index c303ada772..cb6ad788f2 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
@@ -15,16 +15,27 @@ Change the directory to the where the makefile is located.
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/ # replace $HOME with the location of the project
 ```
 
-Using a text editor, for example visual studio code or nano, modify the `APP_TYPE` field in the makefile from the default value of `allon_sensor_tflm` to one of the following. 
+Using a text editor, for example visual studio code or nano, modify the `APP_TYPE` field in the makefile from the default value of `allon_sensor_tflm` to one of the values in the table below 
 
 
-|Parameter|Description|
+|APP_TYPE =|Description|
 |---|---|
 |tflm_folov8_od|Object detection|
 |tflm_folov8_pose|Pose detection|
 |tflm_fd_fm|Face detection|
 
+### Regenerate the Firmware Image
+
 Go back to the 'Build The Firmware' section and start from Step 3.2. to regenerate the firmware image. 
 
 The images below are examples images from the model. 
 
+#### Objection Detection
+![object_detection](./object_detection.jpg)
+
+#### Pose Estimation
+![Pose estimation](./pose_estimation.jpg)
+
+#### Face Detection
+![object_detection](./face_detection.jpg)
+
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/object_detection.jpg b/content/learning-paths/microcontrollers/yolo-on-himax/object_detection.jpg
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From 3a87162ebdff2df2e5b1e867ed6b13712d31f9f7 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Mon, 4 Nov 2024 09:47:11 +0000
Subject: [PATCH 10/87] added reset image and adjusted wording

---
 .../microcontrollers/yolo-on-himax/_index.md   |   6 +++---
 .../yolo-on-himax/_next-steps.md               |  17 ++++++-----------
 .../microcontrollers/yolo-on-himax/how-to-1.md |   6 ++++--
 .../microcontrollers/yolo-on-himax/how-to-3.md |  15 ++++++++++-----
 .../yolo-on-himax/reset_button.jpg             | Bin 0 -> 54671 bytes
 5 files changed, 23 insertions(+), 21 deletions(-)
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/reset_button.jpg

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 21e6533696..dd826d2782 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -6,7 +6,7 @@ minutes_to_complete: 90
 who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U85.
 
 learning_objectives: 
-    - Run Yolo model on off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55
+    - Run Yolo model on off-the-shelf hardware that uses the Arm Cortex-M55 and Ethos-U55
     - Build Himax SDK and generate firmware image file
     - Update firmware on edge device (Himax WiseEye2)
     
@@ -14,7 +14,7 @@ prerequisites:
     - Seeed Grove Vision AI V2 Module
     - OV5647-62 Camera module and included FPC cable
     - A USB-C cable
-    - An Linux-based PC on an x86 archiecture. 
+    - A Linux/Windows-based PC on an x86 archiecture. 
 
 author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
@@ -26,7 +26,7 @@ armips:
     - Ethos U55
 tools_software_languages:
     - Himax SDK
-    - bash scripting
+    - Bash
 operatingsystems:
     - Linux
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md b/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
index c4ae77cc92..b3b4ba6a6e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_next-steps.md
@@ -1,22 +1,17 @@
 ---
-next_step_guidance: PLACEHOLDER TEXT 1
+next_step_guidance: Navigate Machine Learning Development with Ethos-U processors
 
-recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+recommended_path: /learning-paths/microcontrollers/nav-mlek/
 
 further_reading:
     - resource:
-        title: PLACEHOLDER MANUAL 
-        link: PLACEHOLDER MANUAL LINK
+        title: Grove Vision AI Module V2 User Documentation
+        link: https://wiki.seeedstudio.com/grove_vision_ai_v2/
         type: documentation
     - resource:
-        title: PLACEHOLDER BLOG 
-        link: PLACEHOLDER BLOG LINK
+        title: WiseEye2 HX6538 processor blog (SoC powering Grove Vision AI Module V2) 
+        link: https://www.himax.com.tw/products/wiseeye-ai-sensing/wiseeye2-ai-processor/
         type: blog
-    - resource:
-        title: PLACEHOLDER GENERAL WEBSITE 
-        link: PLACEHOLDER GENERAL WEBSITE LINK
-        type: website
-
 
 # ================================================================================
 #       FIXED, DO NOT MODIFY
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index 287372e6a0..b7ddb67739 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -10,7 +10,9 @@ layout: learningpathall
 
 ### Step 1.1. Install Ubuntu
 
-This learning path has been tested on Ubuntu. If you use a Windows computer, it is recommended to install WSL, by searching Ubuntu 22.04.3 LTS in Microsoft store. This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
+If you are running Windows on your host machine, we recommend using Ubuntu through Windows subsystem for Linux 2 (WSL2). Please see [this learning path](https://learn.arm.com/learning-paths/laptops-and-desktops/wsl2/setup/) for assistance  
+
+This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
 
 ```bash
 cat /etc/*release*
@@ -27,7 +29,7 @@ DISTRIB_DESCRIPTION="Ubuntu 22.04.5 LTS"
 
 ### Step 1.2. (Optional) Install Microsoft VS Code
 
-This is only optional. You can use any text editor to view/change code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
+This is only optional. You can use any text editor you are comfortable with to view or edit code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
 
 ### Step 1.3. Install python 3
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index 6a3713ba84..9beb5dd4cc 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -8,8 +8,9 @@ layout: learningpathall
 
 ## Flash The Firmware
 
-### Step 3.1. Install xmodem.
+Now that we have generated a firmware file on our local machine, we need to flash the microcontroller with this firmware. 
 
+### Step 3.1. Install xmodem.
 
 `Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. 
 
@@ -28,14 +29,18 @@ Then, slide the FPC connector in with the metal pins facing down and close the d
 
 ![latched](./latched.jpg)
 
-Then connect the Groove Vision AI V2 Module to your computer via USB-C.
+Then connect the Groove Vision AI V2 Module to your computer via the USB-C cable.
 
-### Step 3.4. Flash the firmware by the following commands:
+### Step 3.4. Flash the firmware onto the moule.
+
+Run the python script below to flash the firmware. 
 
 ```python
 python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img 
 ```
 
-    Note: For each project, the command might be slightly different. See the instructions on project pages for this command.
+    Note: If running one of the other example models demonstrated in '(Optional) Try Different Models', the command might be slightly different.
+
+After the firmware image burning is completed, the message "Do you want to end file transmission and reboot system? (y)" is displayed. Press the reset button on the module as per the image below.
 
-After the firmware image burning is completed, the message "Do you want to end file transmission and reboot system? (y)" is displayed. Press the reset button on module to restart.
+![reset button](./reset_button.jpg)
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/reset_button.jpg b/content/learning-paths/microcontrollers/yolo-on-himax/reset_button.jpg
new file mode 100644
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From 96fe376bad0546a3f1d74b3f7abfd6a3261e0a07 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Mon, 4 Nov 2024 10:01:33 +0000
Subject: [PATCH 11/87] tidied up formatting

---
 .../microcontrollers/yolo-on-himax/_index.md  |  9 ++---
 .../microcontrollers/yolo-on-himax/_review.md | 33 +++----------------
 .../yolo-on-himax/how-to-4.md                 |  4 +--
 3 files changed, 11 insertions(+), 35 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index dd826d2782..61aa41bf0e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -3,12 +3,12 @@ title: Run Yolo computer vision model on a Himax microcontroller
 
 minutes_to_complete: 90
 
-who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U85.
+who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
 
 learning_objectives: 
-    - Run Yolo model on off-the-shelf hardware that uses the Arm Cortex-M55 and Ethos-U55
-    - Build Himax SDK and generate firmware image file
-    - Update firmware on edge device (Himax WiseEye2)
+    - Run a you-only-look-once (Yolo) model using off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55.
+    - Learn how to build the Himax SDK and generate firmware image file.
+    - Learn how to update firmware on edge device (Himax WiseEye2).
     
 prerequisites:
     - Seeed Grove Vision AI V2 Module
@@ -29,6 +29,7 @@ tools_software_languages:
     - Bash
 operatingsystems:
     - Linux
+    - Windows
 
 
 ### FIXED, DO NOT MODIFY
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
index 9b1bfb96fe..27a46683f1 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
@@ -2,38 +2,13 @@
 review:
     - questions:
         question: >
-            PLACEHOLDER QUESTION 1?
+            The Grove Vision AI V2 Module can run Yolov8 model in real time?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
+            - True
+            - False
         correct_answer: 1                    
         explanation: >
-            PLACEHOLDER EXPLANATION 1
-
-    - questions:
-        question: >
-            PLACEHOLDER QUESTION 2?
-        answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 4                   
-        explanation: >
-            PLACEHOLDER EXPLANATION 2
-               
-    - questions:
-        question: >
-            PLACEHOLDER QUESTION 3?
-        answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 2          
-        explanation: >
-            PLACEHOLDER EXPLANATION 3
-
+            The Grove Vision AI V2 Module can run object detection in real time using the Cortex-M55 and Ethos-U55. 
 
 
 # ================================================================================
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index c0fc075bb3..3a9f4e8a96 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -13,11 +13,11 @@ Exit the terminal session and connect the module to the PC via your USB-C cable.
 
 ### Step 4.2. Download the Himax AI web toolkit.  
 
-The Himax AI web toolkit enables a browser-based graphical user interface (GUI) where the live camera feed can be displayed. 
+The Himax AI web toolkit enables a browser-based graphical user interface (GUI) for the live camera feed. 
 
 Download the Himax AI Web toolkit by clicking on this [link](https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip)
 
-Unzip the archived file and double click `index.html.`. This will open the GUI within your default browser.
+Unzip the archived file and double click `index.html`. This will open the GUI within your default browser.
 
 ### Step 4.3. Connect to the Grove Vision AI
 

From 36bfaf39ad347d7c39fbeda7eff6ff23061c8eee Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Mon, 4 Nov 2024 10:24:52 +0000
Subject: [PATCH 12/87] tidied up wording

---
 .../learning-paths/microcontrollers/yolo-on-himax/_index.md   | 4 ++--
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-1.md | 4 ++--
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-2.md | 4 ++--
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-3.md | 4 ++--
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-4.md | 2 +-
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 61aa41bf0e..59d523d0b6 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -1,12 +1,12 @@
 ---
-title: Run Yolo computer vision model on a Himax microcontroller
+title: Run a Computer Vision Model on a Himax Microcontroller
 
 minutes_to_complete: 90
 
 who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
 
 learning_objectives: 
-    - Run a you-only-look-once (Yolo) model using off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55.
+    - Run a you-only-look-once (YOLO) computer vision model using off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55.
     - Learn how to build the Himax SDK and generate firmware image file.
     - Learn how to update firmware on edge device (Himax WiseEye2).
     
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index b7ddb67739..da46268655 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -6,7 +6,7 @@ weight: 2
 layout: learningpathall
 ---
 
-## Set up the development environment
+## Set up the Development Environment
 
 ### Step 1.1. Install Ubuntu
 
@@ -27,7 +27,7 @@ DISTRIB_DESCRIPTION="Ubuntu 22.04.5 LTS"
 ...
 ```
 
-### Step 1.2. (Optional) Install Microsoft VS Code
+### Step 1.2. (Optional) Install Microsoft Visual Studio Code
 
 This is only optional. You can use any text editor you are comfortable with to view or edit code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index 7b21f37c45..388c390f06 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -31,7 +31,7 @@ git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_
 cd Seeed_Grove_Vision_AI_Module_V2
 ```
 
-### Step 2.2. Compile the firmware
+### Step 2.2. Compile the Firmware
 
 The make build tool is used to compile the source code. This should take up around 2-3 minutes depending on the number of CPU cores available.
 
@@ -42,7 +42,7 @@ make
 ```
 
 
-### Step 2.3. Generate firmware image file
+### Step 2.3. Generate a Firmware Image
 
 ```bash
 cd ../we2_image_gen_local/
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index 9beb5dd4cc..b8fde69fd0 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -1,12 +1,12 @@
 ---
-title: Flash Firmware
+title: Flash Firmware onto the Microcontroller
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Flash The Firmware
+## Flash the Firmware
 
 Now that we have generated a firmware file on our local machine, we need to flash the microcontroller with this firmware. 
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index 3a9f4e8a96..2aa5632762 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -21,6 +21,6 @@ Unzip the archived file and double click `index.html`. This will open the GUI wi
 
 ### Step 4.3. Connect to the Grove Vision AI
 
-Select 'Grove Vision AI(V2)` in the top-right hand corner and press connect button. 
+Select 'Grove Vision AI(V2)' in the top-right hand corner and press connect button. 
 
 ![Himax web UI](./himax_web_ui.jpg)

From 6f58ebe28f7d114143f893d66713bad1e5dab121 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 09:11:34 -0500
Subject: [PATCH 13/87] Update _index.md

---
 .../profiling-ml-on-arm/_index.md                     | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 593c7e1116..1c0e05223f 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -1,22 +1,21 @@
 ---
-title: How to Profile ML on Arm
+title: Profile the performance of ML models on Arm
 
 minutes_to_complete: 30
 
-who_is_this_for: Machine Learning App developers who want to see the performance of their models on Arm devices.
+who_is_this_for: This is an introductory topic for software developers who want to learn how to profile the performance of their ML models running on Arm devices.
 
 learning_objectives: 
-    - Profile timings of ML models on Arm devices.
+    - Profile the execution times of ML models on Arm devices.
     - Profile ML application performance on Arm devices.
 
 prerequisites:
-    - Some application development.
-    - Arm device with Android OS.
+    - An Arm-powered Android smartphone.
 
 author_primary: Ben Clark
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Introductory
 subjects: Machine Learning
 armips:
     - Cortex-X

From 1c9ff903299f163763370724055f588be7f3461a Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:11:23 -0500
Subject: [PATCH 14/87] Update _index.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/_index.md        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 1c0e05223f..3bd9669e90 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -16,7 +16,7 @@ author_primary: Ben Clark
 
 ### Tags
 skilllevels: Introductory
-subjects: Machine Learning
+subjects: ML
 armips:
     - Cortex-X
     - Cortex-A

From f87f6dd5ef55dce5feaefe96fed3a6bdd2d6dcfb Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:17:52 -0500
Subject: [PATCH 15/87] Update why-profile.md

---
 .../profiling-ml-on-arm/why-profile.md                 | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 8a0d20d1a6..75ac1724fc 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -1,5 +1,5 @@
 ---
-title: What do you need to profile?
+title: Why do you need to profile your ML application?
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -11,13 +11,13 @@ Working out what is taking the time and memory in your application is the first
 
 With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
 
-We will be looking at an Android example using TFLite in this learning path, but most of the tools also work with Linux to cover a wide range of Arm devices. The principles are the same for other inference engines and platforms, but the tools are different.
+In this Learning Path, you will be profiling at an Android example using TFLite, but most of the tools also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
 
 ## Tools
 
-Currently, you need to use different tools to profile the ML inference or the application's performance as a whole.
+You will need to use different tools to profile the ML inference or the application's performance running on your Arm device.
 
-For profiling the ML inference we will look at [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
+For profiling the ML inference, you will use [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole we will look at [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
 

From 1c8d85fb52f9cbfb7b16f3b1d83ac907052c99de Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:37:16 -0500
Subject: [PATCH 16/87] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md               | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 976a3d0b7b..86fd7702fb 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -1,5 +1,5 @@
 ---
-title: App Profiling with Streamline
+title: Profile your application with Streamline
 weight: 3
 
 ### FIXED, DO NOT MODIFY
@@ -7,26 +7,26 @@ layout: learningpathall
 ---
 
 ## Application Profiling
-Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
+Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer), for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
 
 The profiler can look at memory, CPU activity and cycles, cache misses, and many parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
 ## Streamline
-[Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and then analyzed on the host machine.
+You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
-There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
+There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. This example will be Android based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
 
-For now, as per these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
+First, follow these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), to make sure you have `adb` (Android Debug Bridge) installed. If you have installed [Android Studio](https://developer.android.com/studio), you will have installed adb already. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
+Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the Android SDK `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
 
 Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
-Connect your Android phone to your computer through USB. Ensure that your device is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
+Connect your Android phone to your host machine through USB. Ensure that your Android phone is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
 
-On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
+On your phone, go to `Settings > Developer Options` and enable USB Debugging. If your phone asks you to authorize connection to your host machine, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
 
 Next, you need a debuggable build of the application you want to profile. 
 - In Android Studio, ensure your *Build Variant* is set to `debug`. You can then build the application and install it on your device. 
@@ -34,17 +34,17 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but we intend to add additional useful information first.
+You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
 ## Custom Annotations
 
 In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, you will need to add some files into your project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps.
 
-These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
+These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-For us, we create a file that we will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under our project. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+Create a file that you will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under your project. This will be a wrapper around the Gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -69,7 +69,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so we need a CMakelists.txt file in the same directory as the C file. This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C file. This will look like:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -105,7 +105,7 @@ If you add the following to the `build.gradle` file of the Module you wish to pr
     }
 ```
 
-This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here we create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of our code:
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of your code:
 
 ```kotlin
 // Kotlin wrapper class for integration into Android project
@@ -169,9 +169,9 @@ This colored marker with a string will add the string and time to Streamline's l
 
 In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
-To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
 
-Here is an example setup in a class's companion object.
+Here is an example setup in a class's companion object:
 
 ```kotlin
   companion object {

From e8c1c1bea1ef25dbcbc00dbdfb87e00c8ac6b389 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Thu, 7 Nov 2024 13:19:58 +0000
Subject: [PATCH 17/87] added example (app/network) information

---
 .../profiling-ml-on-arm/_index.md             |  4 +-
 .../app-profiling-streamline.md               | 60 ++++++++++++++++---
 .../nn-profiling-executenetwork.md            | 21 +++++--
 3 files changed, 69 insertions(+), 16 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 3bd9669e90..1271520b0d 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Profile the performance of ML models on Arm
 
-minutes_to_complete: 30
+minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers who want to learn how to profile the performance of their ML models running on Arm devices.
 
@@ -10,7 +10,7 @@ learning_objectives:
     - Profile ML application performance on Arm devices.
 
 prerequisites:
-    - An Arm-powered Android smartphone.
+    - An Arm-powered Android smartphone, and USB cable to connect with it.
 
 author_primary: Ben Clark
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 86fd7702fb..294365aacd 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -13,6 +13,8 @@ The profiler can look at memory, CPU activity and cycles, cache misses, and many
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
+If you want a ready-made application to profile for this section you can use [this example](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference). Clone it to your computer and open it in a recent Android Studio. It is generally safest to not update the Gradle version when asked, to ensure nothing else needs updating. 
+
 ## Streamline
 You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
@@ -34,17 +36,19 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
+If you're using the example application, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+
+You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
 ## Custom Annotations
 
 In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, you will need to add some files into your project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps. If you are using the example project, create a `cpp` folder under the `app/src/main` folder, and add these files there.
 
 These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-Create a file that you will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under your project. This will be a wrapper around the Gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -69,7 +73,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C file. This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). This will look like:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -94,7 +98,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to the `build.gradle` file of the Module you wish to profile, you will be able to call the functions from your Kotlin code:
+If you add the following to the `build.gradle` file of the Module you wish to profile (`:app` in the example), you will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -105,7 +109,7 @@ If you add the following to the `build.gradle` file of the Module you wish to pr
     }
 ```
 
-This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of your code:
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt`. Place the file alongside `MainActivity.kt` in `app\src\main\java\com\arm\armpytorchmnistinference` for the example. Add the following code to `AnnotateStreamline.kt` to enable Kotlin calls to the gator daemon from the rest of your code:
 
 ```kotlin
 // Kotlin wrapper class for integration into Android project
@@ -155,13 +159,17 @@ class AnnotateStreamline {
 }
 ```
 
-The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
+Fill in all the function calls to match the functions you added into `annotate_jni_wrapper.c`.
+
+The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. The first thing is to make sure `AnnotateStreamline.setup()` is called before any other gator functions. For the example project, add it into the `onCreate()` function of `MainActivity.kt`. Then you can add annotations like this:
 
 ```kotlin
       AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Model Load")
 ```
 
-This colored marker with a string will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline:
+In the example app you could add this in the `onCreate()` function of `MainActivity.kt` after the `Module.load()` call to load the `model.pth`.
+
+This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
 
 ![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
 
@@ -188,6 +196,8 @@ Here is an example setup in a class's companion object:
     }
 ```
 
+If you're using the example app, add this to the `MainActivity` class.
+
 Then they can be used like this:
 
 ```kotlin
@@ -201,4 +211,36 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
+In the example app the CAM annotations would be added to the `runInference()` function, which would end up looking like this:
+
+```kotlin
+    private fun runInference(bitmap: Bitmap) {
+        val preprocess = currentId++
+        AnnotateStreamline.camJobStart(camViewId, preprocess, "Preprocess", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_YELLOW)
+        // Convert bitmap to a float array and create a tensor with shape [1, 1, 28, 28]
+        val inputTensor = createTensorFromBitmap(bitmap)   // could add a child CAM job inside function call, but probably too simple
+        AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
+
+        // Run inference and measure time
+        val inferenceTimeMicros = measureTimeMicros {
+            // Forward pass through the model
+            val inference = currentId++
+            AnnotateStreamline.camJobStart(camViewId, inference, "Inference", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_RED)
+            val outputTensor = model.forward(IValue.from(inputTensor)).toTensor()
+            AnnotateStreamline.camJobEnd(camViewId, inference, AnnotateStreamline.getTime())
+            // and then post-processing is simplistic in this case, so not worth a CAM job
+            val scores = outputTensor.dataAsFloatArray
+
+            // Get the index of the class with the highest score
+            val maxIndex = scores.indices.maxByOrNull { scores[it] } ?: -1
+            predictedLabel.text = "Predicted Label: $maxIndex"
+        }
+
+        // Update inference time TextView in microseconds
+        inferenceTime.text = "Inference Time: $inferenceTimeMicros �s"
+    }
+```
+
+The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10�s scale to see the CAM annotations better.
+
+Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index 1254741906..b046364a82 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -11,6 +11,8 @@ One way of running tflite models is with ArmNN. This is available as a delegate
 
 If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
 
+If you want to have a tflite to practice this Learning Path with, you can download one from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). As an example we'll use a [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
+
 To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
 To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
@@ -18,7 +20,8 @@ To run `ExecuteNetwork` you'll need to use `adb` to push the model and the execu
 Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push model.tflite /data/local/tmp/   # the tflite NN model file that you wish to profile
+adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ # if you are using the example tflite model
+# adb push yourmodel.tflite /data/local/tmp/ # where yourmodel is the tflite NN model file that you wish to profile
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
@@ -36,13 +39,17 @@ chmod 777 ExecuteNetwork    # to make the file executable
 chmod 777 *.so	   # to make the library files executable
 ```
 
-Then you can run the model with the following command:
+Then you can run ExecuteNetwork to profile the model. If you are using the example tflite, you can use the following command:
 
 ```bash
-LD_LIBRARY_PATH=. ./ExecuteNetwork -m model.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
+LD_LIBRARY_PATH=. ./ExecuteNetwork -m mobilenet_v2_1.0_224_INT8.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
 ```
 
-This will run the model twice, outputting the layer timings to `modelout.txt`. Specifically, the `-e` and `--output-network-details` flags will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. The `--iterations 2` flag will run the model twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+If you are using your own tflite, replace `mobilenet_v2_1.0_224_INT8.tflite` with the name of your tflite file.
+
+This will run the model twice, outputting the layer timings to `modelout.txt`. The `--iterations 2` flag is the command that means it runs twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+
+For the other flags, of note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
 
 After running the model, you can pull the output file back to your computer with the following commands:
 
@@ -71,4 +78,8 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  
+
+In the mobilenet example output, the graph is from lines 18 to 1629. After this come the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that wants analyzing starts.
+
+The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
\ No newline at end of file

From db80610c6e5e5d5c74204db7f7c5b2a09bb9bfc6 Mon Sep 17 00:00:00 2001
From: Koki Mitsunami <koki.mitsunami@arm.com>
Date: Thu, 7 Nov 2024 22:23:15 +0000
Subject: [PATCH 18/87] create win-opencv content

---
 .../win-opencv/1-opencv-compilers.md          |  79 +++++++++
 .../win-opencv/2-1-build-msvc.md              | 166 ++++++++++++++++++
 .../win-opencv/2-2-build-clang.md             | 159 +++++++++++++++++
 .../laptops-and-desktops/win-opencv/_index.md |  36 ++++
 .../win-opencv/_next-steps.md                 |  27 +++
 .../win-opencv/_review.md                     |  45 +++++
 6 files changed, 512 insertions(+)
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/_index.md
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/_review.md

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
new file mode 100644
index 0000000000..2a32df9280
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
@@ -0,0 +1,79 @@
+---
+title: OpenCV and Compilers
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## OpenCV
+OpenCV (Open Source Computer Vision Library) is a popular, open-source library that developers use to build computer vision applications. It provides a set of tools and functions that help you handle tasks related to images and videos without needing to write everything from scratch. 
+
+Here’s what developers should know:
+
+* __Ease of Use__: OpenCV comes with pre-built functions for common tasks like reading, displaying, and processing images and videos. This saves time compared to writing algorithms from the ground up.
+
+* __Image Processing__: You can perform operations like changing colors, applying filters, resizing, rotating, and other transformations to images with minimal code.
+
+* __Video Handling__: Developers can use OpenCV to capture, modify, and analyze video frames, making it ideal for creating applications like video surveillance or video editing tools.
+
+* __Computer Vision Algorithms__: OpenCV includes built-in algorithms for complex tasks like object detection (e.g., face and eye recognition), edge detection, and image segmentation.
+
+* __Machine Learning__: It includes modules for training models using basic machine learning algorithms, which can be applied for pattern recognition and data analysis in visual data.
+
+* __Community and Resources__: Being open-source and widely adopted, there is a large community of developers contributing to and supporting OpenCV. This makes it easier to find tutorials, documentation, and answers to questions.
+
+
+## Compilers for Windows on Arm Development
+
+When building applications for Windows on Arm, both MSVC (Microsoft Visual C++) and Clang are options for developers, each with its own advantages.
+
+* __MSVC__: A compiler developed by Microsoft that’s part of the Visual Studio IDE. It’s designed specifically for Windows and integrates well with the Windows development ecosystem.
+
+* __Clang__: An open-source compiler that is part of the LLVM project. It’s known for its modern design and and cross-platform capabilities. 
+
+MSVC is the go-to for Windows-focused projects needing seamless integration with Visual Studio. Clang is ideal for cross-platform projects or when using modern C++ features with flexibility. 
+
+## Before you begin
+
+Any Windows on Arm computer which has the required tools installed can be used for this Learning Path. You will learn the build methods using both MSVC and Clang. 
+
+Please install the following tools required for both methods.
+
+* [CMake](/install-guides/cmake)
+
+{{% notice Note %}}
+The instructions were tested with the version 3.28.1
+{{% /notice %}}
+
+* [Git](https://git-scm.com/downloads/win)
+
+{{% notice Note %}}
+There is currently no Arm version of Git. Install the 64-bit x86 version.
+{{% /notice %}}
+
+Follow the link to intall the required tools for a method using MSVC.
+
+* [Visual Studio 2022 or higher](/install-guides/vs-woa). 
+
+{{% notice Note %}}
+The instructions were tested with Visual Studio 2022.
+{{% /notice %}}
+
+To build using Clang, please install the following.
+
+* [LLVM](install-guides/llvm-woa/)
+
+{{% notice Note %}}
+The instructions were tested with the version 18.1.8.
+{{% /notice %}}
+
+* [Ninja]( https://github.com/ninja-build/ninja/releases)
+
+{{% notice Note %}}
+The instructions were tested with version 1.11.1
+{{% /notice %}}
+
+You use the LLVM Clang and the Ninja generator to build. Set PATH to the paths to your LLVM and Ninja install.
+
+You now have the required development tools installed.Please proceed to the page for the compiler you want to build with.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
new file mode 100644
index 0000000000..ed93fd4c57
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
@@ -0,0 +1,166 @@
+---
+title: Build OpenCV Applications with MSVC
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Build OpenCV with MSVC
+
+### Clone OpenCV repo
+
+Open up a Windows Powershell and checkout the source tree:
+
+```bash
+git clone https://github.com/opencv/opencv
+cd opencv
+git checkout tags/4.10.0
+```
+
+{{% notice Note %}}
+You might be able to use a later version. These steps have been tested with the version 4.10.0.
+{{% /notice %}}
+
+### Build with MSVC
+
+Here, you will use CMake from the command line. First, Run the following command to run the pre-build configuration. 
+
+```bash
+mkdir build_msvc
+cd build_msvc
+
+cmake `
+-S .. `
+-B . `
+-G "Visual Studio 17 2022" `
+-DCMAKE_BUILD_TYPE=Release `
+-DBUILD_opencv_world=ON `
+-DWITH_ITT=OFF `
+-DWITH_OPENCL=OFF `
+-DWITH_OPENCLAMDBLAS=OFF `
+-DWITH_OPENCLAMDFFT=OFF `
+-DWITH_OPENCL_D3D11_NV=OFF `
+-DWITH_DIRECTML=OFF `
+-DWITH_DIRECTX=OFF `
+-DWITH_ADE=OFF `
+-DWITH_CAROTENE=OFF
+```
+
+The given options specify the following:
+- The source code is located one level above the current directory.
+- The build will be performed in the current directory.
+- The Visual Studio 2022 MSVC compiler will be used as the compiler.
+- The built library is generated as a single file that includes all of OpenCV's functionality.
+- Disable unnecessary options, assuming processing on Arm CPU.Unnecessary options have been disabled to execute processing on Arm CPUs.
+
+
+If the configuration is successful, a message similar to the following should be displayed at the end of the execution:
+
+```
+-- General configuration for OpenCV 4.10.0 =====================================
+--   Version control:               4.10.0
+--
+--   Platform:
+--     Timestamp:                   2024-11-06T17:47:31Z
+--     Host:                        Windows 10.0.22631 ARM64
+--     CMake:                       3.28.1
+--     CMake generator:             Visual Studio 17 2022
+--     CMake build tool:            C:/Program Files/Microsoft Visual Studio/2022/Professional/MSBuild/Current/Bin/arm64/MSBuild.exe
+--     MSVC:                        1941
+--     Configuration:               Release
+--
+--   CPU/HW features:
+--     Baseline:                    NEON
+--       requested:                 NEON FP16
+--     Dispatched code generation:  NEON_DOTPROD NEON_FP16
+--       requested:                 NEON_FP16 NEON_BF16 NEON_DOTPROD
+--       NEON_DOTPROD (1 files):    + NEON_DOTPROD
+--       NEON_FP16 (2 files):       + NEON_FP16
+--
+--   C/C++:
+--     Built as dynamic libs?:      YES
+--     C++ standard:                11
+--     C++ Compiler:                C:/Program Files/Microsoft Visual Studio/2022/Professional/VC/Tools/MSVC/14.41.34120/bin/Hostarm64/arm64/cl.exe  (ver 19.41.34123.0)
+[...]
+--     C Compiler:                  C:/Program Files/Microsoft Visual Studio/2022/Professional/VC/Tools/MSVC/14.41.34120/bin/Hostarm64/arm64/cl.exe
+[...]
+--   Install to:                    C:/Users/kokmit01/work/opencv/build_msvc/install
+-- -----------------------------------------------------------------
+--
+-- Configuring done (93.6s)
+-- Generating done (2.8s)
+-- Build files have been written to: C:/Users/kokmit01/work/opencv/build_msvc
+```
+
+Now run the following command to build and install:
+
+```bash
+cmake --build . --config Release
+cmake --build . --target INSTALL --config Release
+```
+
+{{% notice Note %}}
+The build takes approximately 20 mins on Lenovo X13s
+{{% /notice %}}
+
+When the build and the install is complete, confirm the shared library have been created:
+
+```bash { output_lines = "2-12,14-22" }
+ls ./install/x64/vc17/bin
+    Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\bin
+Mode                 LastWriteTime         Length Name
+----                 -------------         ------ ----
+-a----        07/11/2024     21:56          43008 opencv_annotation.exe
+-a----        07/11/2024     21:56         143872 opencv_interactive-calibration.exe
+-a----        07/11/2024     21:56          41984 opencv_model_diagnostics.exe
+-a----        07/11/2024     22:03          36864 opencv_version.exe
+-a----        07/11/2024     22:03          35328 opencv_version_win32.exe
+-a----        07/11/2024     16:11       26391552 opencv_videoio_ffmpeg4100_64.dll
+-a----        07/11/2024     22:03          56320 opencv_visualisation.exe
+-a----        07/11/2024     21:56       27175936 opencv_world4100.dll
+ls ./install/x64/vc17/lib
+    Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\lib
+Mode                 LastWriteTime         Length Name
+----                 -------------         ------ ----
+-a----        07/11/2024     16:11            434 OpenCVConfig-version.cmake
+-a----        07/11/2024     16:11          15260 OpenCVConfig.cmake
+-a----        07/11/2024     16:11            972 OpenCVModules-release.cmake
+-a----        07/11/2024     16:11           3879 OpenCVModules.cmake
+-a----        07/11/2024     21:56        2849862 opencv_world4100.lib
+```
+
+{{% notice Note %}}
+The directory name in the middle is "x64," but there is no need to worry as the generated libraries and executable files will definitely run as ARM64.
+{{% /notice %}}
+
+```bash { output_lines = "2" }
+./install/x64/vc17/bin/pencv_version.exe
+4.10.0
+```
+
+## Build OpenCV Applications
+
+
+Add C:\Users\kokmit01\work\opencv\build\install\x64\vc17\bin to PATH environment variable (for access to dll)
+put the dll to the directory with exe file if it doesn't work. 
+
+Set include dir/linker path
+
+
+
+```cpp
+#include <opencv2/opencv.hpp>
+#include <iostream>
+int main() {
+    cv::Mat image = cv::Mat::zeros(100, 100, CV_8UC3);
+    if (image.empty()) {
+        std::cout << "Failed to create an image!" << std::endl;
+        return -1;
+    }
+    cv::circle(image, cv::Point(50, 50), 30, cv::Scalar(255, 0, 0), -1);
+    cv::imwrite("test_image.png", image);
+    cv::waitKey(0);
+    return 0;
+}
+```
\ No newline at end of file
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
new file mode 100644
index 0000000000..7b8591bcba
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -0,0 +1,159 @@
+---
+title: Build OpenCV Applications with Clang
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Build OpenCV with Clang
+
+### Clone OpenCV repo
+
+Open up a Windows Powershell and checkout the source tree:
+
+```bash
+git clone https://github.com/opencv/opencv
+cd opencv
+git checkout tags/4.10.0
+```
+
+{{% notice Note %}}
+You might be able to use a later version. These steps have been tested with the version 4.10.0.
+{{% /notice %}}
+
+### Build with Clang
+
+Here, you will use CMake from the command line. First, Run the following command to run the pre-build configuration. 
+
+```bash
+mkdir build_clang
+cd build_clang
+
+cmake `
+-S .. `
+-B . `
+-G "Ninja" `
+-DCMAKE_C_COMPILER=clang `
+-DCMAKE_CXX_COMPILER=clang++ `
+-DCMAKE_BUILD_TYPE=Release `
+-DBUILD_opencv_world=ON `
+-DWITH_ITT=OFF `
+-DWITH_OPENCL=OFF `
+-DWITH_OPENCLAMDBLAS=OFF `
+-DWITH_OPENCLAMDFFT=OFF `
+-DWITH_OPENCL_D3D11_NV=OFF `
+-DWITH_DIRECTML=OFF `
+-DWITH_DIRECTX=OFF `
+-DWITH_ADE=OFF `
+-DWITH_CAROTENE=OFF
+```
+
+The given options specify the following:
+- The source code is located one level above the current directory.
+- The build will be performed in the current directory.
+- The Visual Studio 2022 MSVC compiler will be used as the compiler.
+- The built library is generated as a single file that includes all of OpenCV's functionality.
+- Disable unnecessary options, assuming processing on Arm CPU.Unnecessary options have been disabled to execute processing on Arm CPUs.
+
+
+If the configuration is successful, a message similar to the following should be displayed at the end of the execution:
+
+```
+-- General configuration for OpenCV 4.10.0 =====================================
+--   Version control:               4.10.0
+--
+--   Platform:
+--     Timestamp:                   2024-11-06T17:47:31Z
+--     Host:                        Windows 10.0.22631 ARM64
+--     CMake:                       3.28.1
+--     CMake generator:             Ninja
+--     CMake build tool:            C:/Users/kokmit01/work/venv/Scripts/ninja.exe
+--     Configuration:               Release
+--
+--   CPU/HW features:
+--     Baseline:                    NEON
+--       requested:                 NEON FP16
+--     Dispatched code generation:  NEON_DOTPROD NEON_FP16
+--       requested:                 NEON_FP16 NEON_BF16 NEON_DOTPROD
+--       NEON_DOTPROD (1 files):    + NEON_DOTPROD
+--       NEON_FP16 (2 files):       + NEON_FP16
+--
+--   C/C++:
+--     Built as dynamic libs?:      YES
+--     C++ standard:                11
+--     C++ Compiler:                C:/Program Files/LLVM/bin/clang++.exe  (ver 18.1.8)
+[...]
+--     C Compiler:                  C:/Program Files/LLVM/bin/clang.exe
+[...]
+--   Install to:                    C:/Users/kokmit01/work/opencv/build_clang/install
+-- -----------------------------------------------------------------
+--
+-- Configuring done (384.4s)
+-- Generating done (1.9s)
+-- Build files have been written to: C:/Users/kokmit01/work/opencv/build_clang
+```
+
+Now run the following command to build and install:
+
+```bash
+ninja
+ninja install
+```
+
+{{% notice Note %}}
+The build takes approximately 25 mins on Lenovo X13s
+{{% /notice %}}
+
+When the build and the install is complete, confirm the shared library have been created:
+
+```bash { output_lines = "2-6,8-16" }
+ls ./install/bin
+    Directory: C:\Users\kokmit01\work\opencv\build_clang\install\bin
+Mode                 LastWriteTime         Length Name
+----                 -------------         ------ ----
+-a----        07/11/2024     16:11       26391552 opencv_videoio_ffmpeg4100_64.dll
+-a----        07/11/2024     21:56       27175936 opencv_world4100.dll
+ls ./install/x64/vc17/lib
+    Directory: C:\Users\kokmit01\work\opencv\build_clang\install\lib
+Mode                 LastWriteTime         Length Name
+----                 -------------         ------ ----
+-a----        07/11/2024     16:11            434 OpenCVConfig-version.cmake
+-a----        07/11/2024     16:11          15260 OpenCVConfig.cmake
+-a----        07/11/2024     16:11            972 OpenCVModules-release.cmake
+-a----        07/11/2024     16:11           3879 OpenCVModules.cmake
+-a----        07/11/2024     21:56        2849862 opencv_world4100.lib
+```
+
+## Build OpenCV Applications
+
+
+Add C:\Users\kokmit01\work\opencv\build\install\bin to PATH environment variable (for access to dll)
+put the dll to the directory with exe file if it doesn't work. 
+
+Set include dir/linker path
+
+
+
+```cpp
+#include <opencv2/opencv.hpp>
+#include <iostream>
+int main() {
+    cv::Mat image = cv::Mat::zeros(100, 100, CV_8UC3);
+    if (image.empty()) {
+        std::cout << "Failed to create an image!" << std::endl;
+        return -1;
+    }
+    cv::circle(image, cv::Point(50, 50), 30, cv::Scalar(255, 0, 0), -1);
+    //cv::imshow("Test Image", image);
+    cv::imwrite("test_image.png", image);
+    cv::waitKey(0);
+    return 0;
+}
+```
+
+
+
+```bash
+ clang++ .\test_opencv.cpp -o test_opencv.exe -I.\build_clang\install\include -L.\build_clang\install\lib -lopencv_world4100
+```
\ No newline at end of file
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
new file mode 100644
index 0000000000..dae9a04626
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -0,0 +1,36 @@
+---
+title: Build OpenCV applications on Windows on Arm
+
+minutes_to_complete: 60
+
+who_is_this_for: This is an advanced topic for software developers who want to use OpenCV with Windows on Arm devices.
+
+learning_objectives: 
+    - Build OpenCV library with Windows on Arm devices.
+    - Develop applications with OpenCV.
+
+prerequisites:
+    - A Windows on Arm computer such as Lenovo Thinkpad X13s or a [virtual machine](/learning-paths/cross-platform/woa_azure/).
+
+author_primary: Koki Mitsunami
+
+### Tags
+skilllevels: Introductory
+subjects: Migration to Arm
+armips:
+    - Cortex-A
+tools_software_languages:
+    - Visual Studio
+    - Clang
+    - OpenCV
+    - C++
+operatingsystems:
+    - Windows
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
new file mode 100644
index 0000000000..c4ae77cc92
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
@@ -0,0 +1,27 @@
+---
+next_step_guidance: PLACEHOLDER TEXT 1
+
+recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+
+further_reading:
+    - resource:
+        title: PLACEHOLDER MANUAL 
+        link: PLACEHOLDER MANUAL LINK
+        type: documentation
+    - resource:
+        title: PLACEHOLDER BLOG 
+        link: PLACEHOLDER BLOG LINK
+        type: blog
+    - resource:
+        title: PLACEHOLDER GENERAL WEBSITE 
+        link: PLACEHOLDER GENERAL WEBSITE LINK
+        type: website
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 21                  # set to always be larger than the content in this path, and one more than 'review'
+title: "Next Steps"         # Always the same
+layout: "learningpathall"   # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
new file mode 100644
index 0000000000..9b1bfb96fe
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
@@ -0,0 +1,45 @@
+---
+review:
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 1?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+        correct_answer: 1                    
+        explanation: >
+            PLACEHOLDER EXPLANATION 1
+
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 2?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 4                   
+        explanation: >
+            PLACEHOLDER EXPLANATION 2
+               
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 3?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 2          
+        explanation: >
+            PLACEHOLDER EXPLANATION 3
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+title: "Review"                 # Always the same title
+weight: 20                      # Set to always be larger than the content in this path
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+---

From 18a11d244e2572f8659e1263f42f514bdeb5a43b Mon Sep 17 00:00:00 2001
From: Koki Mitsunami <koki.mitsunami@arm.com>
Date: Fri, 8 Nov 2024 13:35:31 +0000
Subject: [PATCH 19/87] add how to build app and update description

---
 .../win-opencv/1-opencv-compilers.md          |   8 +-
 .../win-opencv/2-1-build-msvc.md              | 119 +++++++++++++-----
 .../win-opencv/2-2-build-clang.md             | 101 +++++++++++----
 .../laptops-and-desktops/win-opencv/_index.md |   2 +-
 .../win-opencv/_next-steps.md                 |  22 ++--
 .../win-opencv/_review.md                     |  35 +++---
 .../win-opencv/msvc_add_file.png              | Bin 0 -> 80336 bytes
 .../win-opencv/msvc_include_dir.png           | Bin 0 -> 66647 bytes
 .../win-opencv/msvc_link_dir.png              | Bin 0 -> 62935 bytes
 .../win-opencv/msvc_link_lib.png              | Bin 0 -> 50462 bytes
 .../win-opencv/msvc_project.png               | Bin 0 -> 31413 bytes
 .../win-opencv/msvc_screen.png                | Bin 0 -> 106708 bytes
 .../win-opencv/set_path.png                   | Bin 0 -> 30462 bytes
 .../win-opencv/test_image.png                 | Bin 0 -> 493 bytes
 14 files changed, 199 insertions(+), 88 deletions(-)
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_add_file.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_include_dir.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_link_dir.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_link_lib.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_project.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/msvc_screen.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/set_path.png
 create mode 100644 content/learning-paths/laptops-and-desktops/win-opencv/test_image.png

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
index 2a32df9280..23001219e6 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
@@ -1,5 +1,5 @@
 ---
-title: OpenCV and Compilers
+title: OpenCV and Compilers for Windows on Arm
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -30,13 +30,13 @@ When building applications for Windows on Arm, both MSVC (Microsoft Visual C++)
 
 * __MSVC__: A compiler developed by Microsoft that’s part of the Visual Studio IDE. It’s designed specifically for Windows and integrates well with the Windows development ecosystem.
 
-* __Clang__: An open-source compiler that is part of the LLVM project. It’s known for its modern design and and cross-platform capabilities. 
+* __Clang__: An open-source compiler that is part of the LLVM project. It’s known for its modern design and cross-platform capabilities. 
 
 MSVC is the go-to for Windows-focused projects needing seamless integration with Visual Studio. Clang is ideal for cross-platform projects or when using modern C++ features with flexibility. 
 
 ## Before you begin
 
-Any Windows on Arm computer which has the required tools installed can be used for this Learning Path. You will learn the build methods using both MSVC and Clang. 
+Any Windows on Arm machine which has the required tools installed can be used for this Learning Path. You will learn the build methods using both MSVC and Clang. 
 
 Please install the following tools required for both methods.
 
@@ -76,4 +76,4 @@ The instructions were tested with version 1.11.1
 
 You use the LLVM Clang and the Ninja generator to build. Set PATH to the paths to your LLVM and Ninja install.
 
-You now have the required development tools installed.Please proceed to the page for the compiler you want to build with.
+You now have the required development tools installed. Please proceed to the page for the compiler you want to build with.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
index ed93fd4c57..8773c603e8 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
@@ -22,9 +22,9 @@ git checkout tags/4.10.0
 You might be able to use a later version. These steps have been tested with the version 4.10.0.
 {{% /notice %}}
 
-### Build with MSVC
+### Pre-build configuration
 
-Here, you will use CMake from the command line. First, Run the following command to run the pre-build configuration. 
+Here, you will use CMake from the command line. First, run the following command to run the pre-build configuration. 
 
 ```bash
 mkdir build_msvc
@@ -52,8 +52,9 @@ The given options specify the following:
 - The build will be performed in the current directory.
 - The Visual Studio 2022 MSVC compiler will be used as the compiler.
 - The built library is generated as a single file that includes all of OpenCV's functionality.
-- Disable unnecessary options, assuming processing on Arm CPU.Unnecessary options have been disabled to execute processing on Arm CPUs.
+- Unnecessary options have been disabled, assuming processing on Arm CPUs.
 
+&nbsp;
 
 If the configuration is successful, a message similar to the following should be displayed at the end of the execution:
 
@@ -62,13 +63,13 @@ If the configuration is successful, a message similar to the following should be
 --   Version control:               4.10.0
 --
 --   Platform:
---     Timestamp:                   2024-11-06T17:47:31Z
+--     Timestamp:                   2024-11-08T08:50:24Z
 --     Host:                        Windows 10.0.22631 ARM64
 --     CMake:                       3.28.1
 --     CMake generator:             Visual Studio 17 2022
 --     CMake build tool:            C:/Program Files/Microsoft Visual Studio/2022/Professional/MSBuild/Current/Bin/arm64/MSBuild.exe
 --     MSVC:                        1941
---     Configuration:               Release
+--     Configuration:               Debug Release
 --
 --   CPU/HW features:
 --     Baseline:                    NEON
@@ -88,11 +89,13 @@ If the configuration is successful, a message similar to the following should be
 --   Install to:                    C:/Users/kokmit01/work/opencv/build_msvc/install
 -- -----------------------------------------------------------------
 --
--- Configuring done (93.6s)
+-- Configuring done (97.5s)
 -- Generating done (2.8s)
 -- Build files have been written to: C:/Users/kokmit01/work/opencv/build_msvc
 ```
 
+### Build and install
+
 Now run the following command to build and install:
 
 ```bash
@@ -101,53 +104,72 @@ cmake --build . --target INSTALL --config Release
 ```
 
 {{% notice Note %}}
-The build takes approximately 20 mins on Lenovo X13s
+The build takes approximately 25 mins on Lenovo X13s
 {{% /notice %}}
 
+&nbsp;
+
 When the build and the install is complete, confirm the shared library have been created:
 
-```bash { output_lines = "2-12,14-22" }
+```bash { output_lines = "2-12,15-22" }
 ls ./install/x64/vc17/bin
     Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\bin
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
--a----        07/11/2024     21:56          43008 opencv_annotation.exe
--a----        07/11/2024     21:56         143872 opencv_interactive-calibration.exe
--a----        07/11/2024     21:56          41984 opencv_model_diagnostics.exe
--a----        07/11/2024     22:03          36864 opencv_version.exe
--a----        07/11/2024     22:03          35328 opencv_version_win32.exe
--a----        07/11/2024     16:11       26391552 opencv_videoio_ffmpeg4100_64.dll
--a----        07/11/2024     22:03          56320 opencv_visualisation.exe
--a----        07/11/2024     21:56       27175936 opencv_world4100.dll
+-a----        08/11/2024     09:03          43008 opencv_annotation.exe
+-a----        08/11/2024     09:03         143872 opencv_interactive-calibration.exe
+-a----        08/11/2024     09:03          41984 opencv_model_diagnostics.exe
+-a----        08/11/2024     09:12          36864 opencv_version.exe
+-a----        08/11/2024     09:12          35328 opencv_version_win32.exe
+-a----        08/11/2024     08:50       26391552 opencv_videoio_ffmpeg4100_64.dll
+-a----        08/11/2024     09:12          56320 opencv_visualisation.exe
+-a----        08/11/2024     09:03       27179008 opencv_world4100.dll
+
 ls ./install/x64/vc17/lib
     Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\lib
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
--a----        07/11/2024     16:11            434 OpenCVConfig-version.cmake
--a----        07/11/2024     16:11          15260 OpenCVConfig.cmake
--a----        07/11/2024     16:11            972 OpenCVModules-release.cmake
--a----        07/11/2024     16:11           3879 OpenCVModules.cmake
--a----        07/11/2024     21:56        2849862 opencv_world4100.lib
+-a----        08/11/2024     08:50            434 OpenCVConfig-version.cmake
+-a----        08/11/2024     08:50          15260 OpenCVConfig.cmake
+-a----        08/11/2024     08:50            972 OpenCVModules-release.cmake
+-a----        08/11/2024     08:50           3879 OpenCVModules.cmake
+-a----        08/11/2024     09:02        2849862 opencv_world4100.lib
 ```
 
-{{% notice Note %}}
-The directory name in the middle is "x64," but there is no need to worry as the generated libraries and executable files will definitely run as ARM64.
-{{% /notice %}}
+&nbsp;
+
+`opencv_world<version>.lib/dll` will be the library used by your application. Once the library files are correctly generated, run the following command to ensure there are no errors.
 
 ```bash { output_lines = "2" }
-./install/x64/vc17/bin/pencv_version.exe
+./install/x64/vc17/bin/opencv_version.exe
 4.10.0
 ```
 
+{{% notice Note %}}
+The genereated directory name contains "x64," but there is no need to worry as the libraries and executable files will definitely run as ARM64.
+{{% /notice %}}
+
+&nbsp;
+
 ## Build OpenCV Applications
 
+Once the OpenCV library has been successfully built, the next step is to link it to a simple application and try using it.
+
+### Create a new project in Visual Studio
+
+First, create a new project in Visual Studio. Launch Visual Studio, click `Create a new project` on the initial screen, then select `Empty Project` and click `Next`. On the next screen, set the `Project name` and `Location`. You can choose any name and location, but for this example, we named the project `TestOpenCV`, as shown below. Then click `Create` to generate the new project.
+
+![MSVC project](msvc_project.png "Create a new project")
+
+### Adding a source code
 
-Add C:\Users\kokmit01\work\opencv\build\install\x64\vc17\bin to PATH environment variable (for access to dll)
-put the dll to the directory with exe file if it doesn't work. 
+ In `Solution Explorer`, right-click the `Source Files` folder, select `Add`, and then `New Item...`. Create a file named `test_opencv.cpp`.
 
-Set include dir/linker path
+![MSVC add file](msvc_add_file.png "Add a source file")
 
+&nbsp;
 
+Once the file is created, it will open in the editor. Copy and paste the following program into it and save the file.
 
 ```cpp
 #include <opencv2/opencv.hpp>
@@ -163,4 +185,43 @@ int main() {
     cv::waitKey(0);
     return 0;
 }
-```
\ No newline at end of file
+```
+
+This program is a simple example that uses OpenCV's functionality to create a 100x100 black image, draw a blue circle on it, and save it as a file.
+
+### Configure build settings
+
+Next, select the `Configuration` dropdown menu in the center of the screen and change it from `Debug` to `Release`. At this stage, your screen should look like the example shown below.
+
+![MSVC screenshot](msvc_screen.png "MSVC screenshot")
+
+&nbsp;
+
+Now, set up the compile and link settings. Select `Project` from the top menu and click on `TestOpenCV properties`. Edit `Include directories`, `Library directories`, and `Additional dependencies` as shown in the images below, and then click OK.
+
+![MSVC include dir](msvc_include_dir.png "Include directories: Specify the directory containing the OpenCV header files.")
+
+&nbsp;
+
+![MSVC link dir](msvc_link_dir.png "Library directories: Specify the directory where the libraries for linking are located.")
+
+&nbsp;
+
+![MSVC link lib](msvc_link_lib.png "Additional dependencies: Specify the names of the libraries to link")
+
+&nbsp;
+
+Finally, ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable. Set this in the Windows system settings. After setting the environment variable, restart Visual Studio to apply the changes.
+
+![path setting](set_path.png "Set the DLL dir to the PATH environment variable")
+
+### Run the build
+
+Once these steps are complete, you're ready to build. From the top menu, select `Debug` and click `Start Without Debugging` or press `Ctrl` + `F5`.
+
+If a console window appears showing that the program exited with code 0 and `test_image.png` is generated in the top-level directory of your Visual Studio project, you have succeeded. When you open the image file, it should look like the example shown below.
+
+![test_image pic](test_image.png "test_image.png")
+
+Congratulations! You are now ready to create your own OpenCV applications.
+
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
index 7b8591bcba..58e8b81bcd 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -22,9 +22,9 @@ git checkout tags/4.10.0
 You might be able to use a later version. These steps have been tested with the version 4.10.0.
 {{% /notice %}}
 
-### Build with Clang
+### Pre-build configuration
 
-Here, you will use CMake from the command line. First, Run the following command to run the pre-build configuration. 
+Here, you will use CMake from the command line. First, run the following command to run the pre-build configuration. 
 
 ```bash
 mkdir build_clang
@@ -52,10 +52,11 @@ cmake `
 The given options specify the following:
 - The source code is located one level above the current directory.
 - The build will be performed in the current directory.
-- The Visual Studio 2022 MSVC compiler will be used as the compiler.
+- The Clang compiler will be used as the compiler.
 - The built library is generated as a single file that includes all of OpenCV's functionality.
-- Disable unnecessary options, assuming processing on Arm CPU.Unnecessary options have been disabled to execute processing on Arm CPUs.
+- Unnecessary options have been disabled, assuming processing on Arm CPUs.
 
+&nbsp;
 
 If the configuration is successful, a message similar to the following should be displayed at the end of the execution:
 
@@ -64,7 +65,7 @@ If the configuration is successful, a message similar to the following should be
 --   Version control:               4.10.0
 --
 --   Platform:
---     Timestamp:                   2024-11-06T17:47:31Z
+--     Timestamp:                   2024-11-08T09:23:57Z
 --     Host:                        Windows 10.0.22631 ARM64
 --     CMake:                       3.28.1
 --     CMake generator:             Ninja
@@ -74,10 +75,11 @@ If the configuration is successful, a message similar to the following should be
 --   CPU/HW features:
 --     Baseline:                    NEON
 --       requested:                 NEON FP16
---     Dispatched code generation:  NEON_DOTPROD NEON_FP16
+--     Dispatched code generation:  NEON_DOTPROD NEON_FP16 NEON_BF16
 --       requested:                 NEON_FP16 NEON_BF16 NEON_DOTPROD
 --       NEON_DOTPROD (1 files):    + NEON_DOTPROD
 --       NEON_FP16 (2 files):       + NEON_FP16
+--       NEON_BF16 (0 files):       + NEON_BF16
 --
 --   C/C++:
 --     Built as dynamic libs?:      YES
@@ -89,11 +91,13 @@ If the configuration is successful, a message similar to the following should be
 --   Install to:                    C:/Users/kokmit01/work/opencv/build_clang/install
 -- -----------------------------------------------------------------
 --
--- Configuring done (384.4s)
--- Generating done (1.9s)
+-- Configuring done (244.5s)
+-- Generating done (1.4s)
 -- Build files have been written to: C:/Users/kokmit01/work/opencv/build_clang
 ```
 
+### Build and install
+
 Now run the following command to build and install:
 
 ```bash
@@ -105,35 +109,51 @@ ninja install
 The build takes approximately 25 mins on Lenovo X13s
 {{% /notice %}}
 
+&nbsp;
+
 When the build and the install is complete, confirm the shared library have been created:
 
-```bash { output_lines = "2-6,8-16" }
+```bash { output_lines = "2-11,13-20" }
 ls ./install/bin
-    Directory: C:\Users\kokmit01\work\opencv\build_clang\install\bin
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
--a----        07/11/2024     16:11       26391552 opencv_videoio_ffmpeg4100_64.dll
--a----        07/11/2024     21:56       27175936 opencv_world4100.dll
-ls ./install/x64/vc17/lib
+-a----        08/11/2024     09:51          40448 opencv_annotation.exe
+-a----        08/11/2024     09:51         126464 opencv_interactive-calibration.exe
+-a----        08/11/2024     09:51          40448 opencv_model_diagnostics.exe
+-a----        08/11/2024     09:51          38400 opencv_version.exe
+-a----        08/11/2024     09:51          35840 opencv_version_win32.exe
+-a----        08/11/2024     09:23       26391552 opencv_videoio_ffmpeg4100_64.dll
+-a----        08/11/2024     09:51          51712 opencv_visualisation.exe
+-a----        08/11/2024     09:50       20207104 opencv_world4100.dll
+ls ./install/lib
     Directory: C:\Users\kokmit01\work\opencv\build_clang\install\lib
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
--a----        07/11/2024     16:11            434 OpenCVConfig-version.cmake
--a----        07/11/2024     16:11          15260 OpenCVConfig.cmake
--a----        07/11/2024     16:11            972 OpenCVModules-release.cmake
--a----        07/11/2024     16:11           3879 OpenCVModules.cmake
--a----        07/11/2024     21:56        2849862 opencv_world4100.lib
+-a----        08/11/2024     09:23            434 OpenCVConfig-version.cmake
+-a----        08/11/2024     09:23          15254 OpenCVConfig.cmake
+-a----        08/11/2024     09:23            936 OpenCVModules-release.cmake
+-a----        08/11/2024     09:23           3749 OpenCVModules.cmake
+-a----        08/11/2024     09:50        2862548 opencv_world4100.lib
 ```
 
-## Build OpenCV Applications
+&nbsp;
 
+`opencv_world<version>.lib/dll` will be the library used by your application. Once the library files are correctly generated, run the following command to ensure there are no errors.
 
-Add C:\Users\kokmit01\work\opencv\build\install\bin to PATH environment variable (for access to dll)
-put the dll to the directory with exe file if it doesn't work. 
+```bash { output_lines = "2" }
+./install/bin/opencv_version.exe
+4.10.0
+```
+
+&nbsp;
+
+## Build OpenCV Applications
 
-Set include dir/linker path
+Once the OpenCV library has been successfully built, the next step is to link it to a simple application and try using it.
 
+### Prepare a application program
 
+First, save the following C++ program as `test_opencv.cpp` in an appropriate location. Here, it is assumed that the file is saved under the `build_clang` directory.
 
 ```cpp
 #include <opencv2/opencv.hpp>
@@ -145,15 +165,46 @@ int main() {
         return -1;
     }
     cv::circle(image, cv::Point(50, 50), 30, cv::Scalar(255, 0, 0), -1);
-    //cv::imshow("Test Image", image);
     cv::imwrite("test_image.png", image);
     cv::waitKey(0);
     return 0;
 }
 ```
 
+This program is a simple example that uses OpenCV's functionality to create a 100x100 black image, draw a blue circle on it, and save it as a file.
 
+### Compile the program
 
+Now, let's compile this code using the command below. 
 ```bash
- clang++ .\test_opencv.cpp -o test_opencv.exe -I.\build_clang\install\include -L.\build_clang\install\lib -lopencv_world4100
-```
\ No newline at end of file
+clang++ .\test_opencv.cpp -o test_opencv.exe -I.\install\include -L.\install\lib -lopencv_world4100
+```
+
+The given options specify the following:
+* __`-o`__: Specifies the name of the generated executable file.
+* __`-I`__: Indicates the directory where the OpenCV header files to be included are located.
+* __`-L`__: Specifies the directory where the libraries for linking are located.
+* __`-l`__: Specifies the name of the library to link. When linking `opencv_world4100.lib`, omit the `.lib` extension and specify it as `-lopencv_world4100`.
+
+### Run the program
+
+Now, let's run it. To run this executable, you need to ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable, or place the DLLs in the same location as the executable.
+
+```bash
+.\test_opencv.exe
+```
+
+{{% notice Note %}}
+The command below adds the DLL directory to the beginning of the `PATH` environment variable. Since this is a temporary setting, the `PATH` will revert to its original state when the PowerShell session is closed. To set it permanently, you need to use the Windows system settings or the `[Environment]::SetEnvironmentVariable()` method.
+
+```bash
+$env:PATH = "./install/bin;" + $env:PATH
+```
+{{% /notice %}}
+
+When you execute the command, it should finish quickly, and `test_image.png` should be generated. If the image file looks like the one shown below, it means you've succeeded.
+
+![test_image pic](test_image.png "test_image.png")
+
+
+Congratulations! You are now ready to create your own OpenCV applications.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index dae9a04626..da15b1c424 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Build OpenCV applications on Windows on Arm
 
-minutes_to_complete: 60
+minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for software developers who want to use OpenCV with Windows on Arm devices.
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
index c4ae77cc92..6870d89e90 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
@@ -1,21 +1,21 @@
 ---
-next_step_guidance: PLACEHOLDER TEXT 1
+next_step_guidance: You have successfully built OpenCV on Windows on Arm using MSVC/Clang. You might be interested in how to create Windows Forms applications on Windows on Arm.
 
-recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+recommended_path: /learning-paths/laptops-and-desktops/win_forms/
 
 further_reading:
     - resource:
-        title: PLACEHOLDER MANUAL 
-        link: PLACEHOLDER MANUAL LINK
-        type: documentation
-    - resource:
-        title: PLACEHOLDER BLOG 
-        link: PLACEHOLDER BLOG LINK
-        type: blog
+        title: Arm Kleidi Libraries
+        link: https://www.arm.com/products/development-tools/embedded-and-software/kleidi-libraries
+        type: website
     - resource:
-        title: PLACEHOLDER GENERAL WEBSITE 
-        link: PLACEHOLDER GENERAL WEBSITE LINK
+        title: OpenCV website
+        link: https://opencv.org/
         type: website
+    - resource:
+        title: Evolution of SIMD architecture with SVE2 
+        link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/sve2
+        type: blog
 
 
 # ================================================================================
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
index 9b1bfb96fe..9f0dd17c46 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
@@ -2,37 +2,36 @@
 review:
     - questions:
         question: >
-            PLACEHOLDER QUESTION 1?
+            What is OpenCV?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-        correct_answer: 1                    
+            - OpenCV is a game development software that helps create 3D video games quickly.
+            - OpenCV is a library that helps computers see and work with images and videos.
+            - OpenCV is a tool used for creating music and sound effects for apps.
+        correct_answer: 2                    
         explanation: >
-            PLACEHOLDER EXPLANATION 1
+            OpenCV is a powerful open-source library that helps computers process and understand images and videos. It is used in tasks like detecting objects and editing images.
 
     - questions:
         question: >
-            PLACEHOLDER QUESTION 2?
+            What is MSVC?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 4                   
+            - MSVC is a tool for creating user interfaces in web development.
+            - MSVC is a Microsoft compiler used for building C/C++ applications, mainly on Windows.
+            - MSVC is a library for machine learning and data analysis.
+        correct_answer: 2                   
         explanation: >
-            PLACEHOLDER EXPLANATION 2
+            MSVC (Microsoft Visual C++) is a compiler provided by Microsoft that is part of Visual Studio. It is widely used for building and compiling C/C++ programs on Windows, offering good integration with Windows libraries and debugging tools.
                
     - questions:
         question: >
-            PLACEHOLDER QUESTION 3?
+            What is Clang?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
+            - Clang is a tool for designing animations for mobile apps.
+            - Clang is a compiler that is part of the LLVM project, known for cross-platform support and detailed error messages.
+            - Clang is a plugin for adding extra features to text editors.
         correct_answer: 2          
         explanation: >
-            PLACEHOLDER EXPLANATION 3
+            Clang is an open-source compiler that is part of the LLVM project. It is known for its support for C/C++ and other languages, cross-platform capabilities, and clear error diagnostics, making it popular for modern development needs.
 
 
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/msvc_add_file.png b/content/learning-paths/laptops-and-desktops/win-opencv/msvc_add_file.png
new file mode 100644
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From fb1124ba3d1cfc9f49caf6f695dda82207c36339 Mon Sep 17 00:00:00 2001
From: Koki Mitsunami <koki.mitsunami@arm.com>
Date: Fri, 8 Nov 2024 13:40:42 +0000
Subject: [PATCH 20/87] update knowledge check

---
 .../laptops-and-desktops/win-opencv/_review.md | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
index 9f0dd17c46..4903c6186e 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
@@ -13,23 +13,21 @@ review:
 
     - questions:
         question: >
-            What is MSVC?
+            MSVC is a Microsoft compiler used for building C/C++ applications, mainly on Windows.
         answers:
-            - MSVC is a tool for creating user interfaces in web development.
-            - MSVC is a Microsoft compiler used for building C/C++ applications, mainly on Windows.
-            - MSVC is a library for machine learning and data analysis.
-        correct_answer: 2                   
+            - "True"
+            - "False"
+        correct_answer: 1                   
         explanation: >
             MSVC (Microsoft Visual C++) is a compiler provided by Microsoft that is part of Visual Studio. It is widely used for building and compiling C/C++ programs on Windows, offering good integration with Windows libraries and debugging tools.
                
     - questions:
         question: >
-            What is Clang?
+            Clang is a compiler that is part of the LLVM project, known for cross-platform support.
         answers:
-            - Clang is a tool for designing animations for mobile apps.
-            - Clang is a compiler that is part of the LLVM project, known for cross-platform support and detailed error messages.
-            - Clang is a plugin for adding extra features to text editors.
-        correct_answer: 2          
+            - "True"
+            - "False"
+        correct_answer: 1         
         explanation: >
             Clang is an open-source compiler that is part of the LLVM project. It is known for its support for C/C++ and other languages, cross-platform capabilities, and clear error diagnostics, making it popular for modern development needs.
 

From c9517709feb5eb03f9bb570c875b91fcf3a84aca Mon Sep 17 00:00:00 2001
From: Koki Mitsunami <koki.mitsunami@arm.com>
Date: Fri, 8 Nov 2024 15:00:05 +0000
Subject: [PATCH 21/87] update image files

---
 .../win-opencv/2-1-build-msvc.md              |  12 +++++++-----
 .../win-opencv/2-2-build-clang.md             |  13 ++++++++-----
 .../win-opencv/msvc_include_dir.png           | Bin 66647 -> 184133 bytes
 .../win-opencv/msvc_link_dir.png              | Bin 62935 -> 163445 bytes
 .../win-opencv/msvc_project.png               | Bin 31413 -> 55115 bytes
 .../win-opencv/set_path.png                   | Bin 30462 -> 34217 bytes
 6 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
index 8773c603e8..3a3c819e9a 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
@@ -86,12 +86,12 @@ If the configuration is successful, a message similar to the following should be
 [...]
 --     C Compiler:                  C:/Program Files/Microsoft Visual Studio/2022/Professional/VC/Tools/MSVC/14.41.34120/bin/Hostarm64/arm64/cl.exe
 [...]
---   Install to:                    C:/Users/kokmit01/work/opencv/build_msvc/install
+--   Install to:                    C:/Users/username/work/opencv/build_msvc/install
 -- -----------------------------------------------------------------
 --
 -- Configuring done (97.5s)
 -- Generating done (2.8s)
--- Build files have been written to: C:/Users/kokmit01/work/opencv/build_msvc
+-- Build files have been written to: C:/Users/username/work/opencv/build_msvc
 ```
 
 ### Build and install
@@ -111,9 +111,9 @@ The build takes approximately 25 mins on Lenovo X13s
 
 When the build and the install is complete, confirm the shared library have been created:
 
-```bash { output_lines = "2-12,15-22" }
+```bash { output_lines = "2-11" }
 ls ./install/x64/vc17/bin
-    Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\bin
+    Directory: C:\Users\username\work\opencv\build_msvc\install\x64\vc17\bin
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
 -a----        08/11/2024     09:03          43008 opencv_annotation.exe
@@ -124,9 +124,11 @@ Mode                 LastWriteTime         Length Name
 -a----        08/11/2024     08:50       26391552 opencv_videoio_ffmpeg4100_64.dll
 -a----        08/11/2024     09:12          56320 opencv_visualisation.exe
 -a----        08/11/2024     09:03       27179008 opencv_world4100.dll
+```
 
+```bash { output_lines = "2-9" }
 ls ./install/x64/vc17/lib
-    Directory: C:\Users\kokmit01\work\opencv\build_msvc\install\x64\vc17\lib
+    Directory: C:\Users\username\work\opencv\build_msvc\install\x64\vc17\lib
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
 -a----        08/11/2024     08:50            434 OpenCVConfig-version.cmake
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
index 58e8b81bcd..d8d8d7ae53 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -69,7 +69,7 @@ If the configuration is successful, a message similar to the following should be
 --     Host:                        Windows 10.0.22631 ARM64
 --     CMake:                       3.28.1
 --     CMake generator:             Ninja
---     CMake build tool:            C:/Users/kokmit01/work/venv/Scripts/ninja.exe
+--     CMake build tool:            C:/Users/username/work/venv/Scripts/ninja.exe
 --     Configuration:               Release
 --
 --   CPU/HW features:
@@ -88,12 +88,12 @@ If the configuration is successful, a message similar to the following should be
 [...]
 --     C Compiler:                  C:/Program Files/LLVM/bin/clang.exe
 [...]
---   Install to:                    C:/Users/kokmit01/work/opencv/build_clang/install
+--   Install to:                    C:/Users/username/work/opencv/build_clang/install
 -- -----------------------------------------------------------------
 --
 -- Configuring done (244.5s)
 -- Generating done (1.4s)
--- Build files have been written to: C:/Users/kokmit01/work/opencv/build_clang
+-- Build files have been written to: C:/Users/username/work/opencv/build_clang
 ```
 
 ### Build and install
@@ -113,7 +113,7 @@ The build takes approximately 25 mins on Lenovo X13s
 
 When the build and the install is complete, confirm the shared library have been created:
 
-```bash { output_lines = "2-11,13-20" }
+```bash { output_lines = "2-11" }
 ls ./install/bin
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
@@ -125,8 +125,11 @@ Mode                 LastWriteTime         Length Name
 -a----        08/11/2024     09:23       26391552 opencv_videoio_ffmpeg4100_64.dll
 -a----        08/11/2024     09:51          51712 opencv_visualisation.exe
 -a----        08/11/2024     09:50       20207104 opencv_world4100.dll
+```
+
+```bash { output_lines = "2-9" }
 ls ./install/lib
-    Directory: C:\Users\kokmit01\work\opencv\build_clang\install\lib
+    Directory: C:\Users\username\work\opencv\build_clang\install\lib
 Mode                 LastWriteTime         Length Name
 ----                 -------------         ------ ----
 -a----        08/11/2024     09:23            434 OpenCVConfig-version.cmake
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/msvc_include_dir.png b/content/learning-paths/laptops-and-desktops/win-opencv/msvc_include_dir.png
index a90ae7c1d91762378bf90e1b44289bdcf2861ba5..821113407476c112aafc1953f6b6c6a9b2e60016 100644
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From 7483342ec0d0b700e781a111a649c547e65b0b99 Mon Sep 17 00:00:00 2001
From: Koki Mitsunami <koki.mitsunami@arm.com>
Date: Mon, 11 Nov 2024 14:09:50 +0000
Subject: [PATCH 22/87] add linkedin account

---
 assets/contributors.csv | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/assets/contributors.csv b/assets/contributors.csv
index ac5348825b..d0e9345629 100644
--- a/assets/contributors.csv
+++ b/assets/contributors.csv
@@ -43,6 +43,6 @@ Rin Dobrescu,Arm,,,,
 Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,,
 Nader Zouaoui,Day Devs,nader-zouaoui,nader-zouaoui,@zouaoui_nader,https://daydevs.com/
 Alaaeddine Chakroun,Day Devs,Alaaeddine-Chakroun,alaaeddine-chakroun,,https://daydevs.com/
-Koki Mitsunami,Arm,,,,
+Koki Mitsunami,Arm,,kmitsunami,,
 Chen Zhang,Zilliz,,,,
 Tianyu Li,Arm,,,,

From c57c3d0f0b390bc857ab3157ee86232600b524e9 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 11:14:56 -0500
Subject: [PATCH 23/87] Update why-profile.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/why-profile.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 75ac1724fc..7d688a4ad6 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -11,7 +11,7 @@ Working out what is taking the time and memory in your application is the first
 
 With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
 
-In this Learning Path, you will be profiling at an Android example using TFLite, but most of the tools also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
+In this Learning Path, you will profile an Android example using TFLite, but most of the steps shown will also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
 
 ## Tools
 
@@ -19,5 +19,5 @@ You will need to use different tools to profile the ML inference or the applicat
 
 For profiling the ML inference, you will use [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and the Android Studio Profiler.
 

From a03f8357d8fd1d833beb79dfb8dccc3174972d8a Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 12:13:30 -0500
Subject: [PATCH 24/87] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md                 | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 294365aacd..a6ebb950b5 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -13,16 +13,19 @@ The profiler can look at memory, CPU activity and cycles, cache misses, and many
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
-If you want a ready-made application to profile for this section you can use [this example](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference). Clone it to your computer and open it in a recent Android Studio. It is generally safest to not update the Gradle version when asked, to ensure nothing else needs updating. 
+## Example Android Application 
+
+In this Learning Path, you will use profile [an example Android application](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference) using Streamline. 
+Start by cloning the repository containing this example on your machine and open it in a recent Android Studio. It is generally safest to not update the Gradle version when prompted. 
 
 ## Streamline
-You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
+You will install Streamline and Performance Studio on your host machine and connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
-There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. This example will be Android based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
+For more details on Streamline usage you can refer to these [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio). While the example you are running is based on Android, you can use [the setup and capture instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application).
 
 First, follow these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), to make sure you have `adb` (Android Debug Bridge) installed. If you have installed [Android Studio](https://developer.android.com/studio), you will have installed adb already. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the Android SDK `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
+Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by installing the [Android SDK `platform-tools`](https://developer.android.com/tools/releases/platform-tools#downloads) directory to your path. 
 
 Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
@@ -36,7 +39,7 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-If you're using the example application, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
 
 You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
@@ -237,10 +240,10 @@ In the example app the CAM annotations would be added to the `runInference()` fu
         }
 
         // Update inference time TextView in microseconds
-        inferenceTime.text = "Inference Time: $inferenceTimeMicros �s"
+        inferenceTime.text = "Inference Time: $inferenceTimeMicros µs"
     }
 ```
 
-The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10�s scale to see the CAM annotations better.
+The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
 
 Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.

From 4289d2e6f895f0aa1a3e839b75281ab0d59058c5 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:12:50 -0500
Subject: [PATCH 25/87] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md               | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index a6ebb950b5..e55e4e172d 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -39,19 +39,19 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install this application on your device.
 
-You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
+You can now run Streamline and [capture a profile](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. But before you do, lets add some useful annotations to your code that can help with more specific performance analysis of your application.
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline, it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps. If you are using the example project, create a `cpp` folder under the `app/src/main` folder, and add these files there.
+To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are named `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` and made available [here](https://github.com/ARM-software/gator/tree/main/annotate). Using these annotations, you will be able to show log strings, markers, counters and Custom Activity Maps. WIthin your example project, create a `cpp` folder under the `app/src/main` folder, and add these three files there.
 
 These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other gator daemon functions you want.
+Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. Copy the code below into this file. You can also create very similar wrapper functions for other gator daemon functions.
 
 ```c
 #include <jni.h>
@@ -66,7 +66,7 @@ JNIEXPORT jlong JNICALL Java_AnnotateStreamline_GetTime(JNIEnv* env, jobject obj
 }
 ```
 
-Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion:
+Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion as shown below:
 
 ```c
 JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* env, jobject obj, jint color, jstring str) {
@@ -76,7 +76,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). Copy the contents shown below into `CMakelists.txt`:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -101,7 +101,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to the `build.gradle` file of the Module you wish to profile (`:app` in the example), you will be able to call the functions from your Kotlin code:
+Now add the code below to the `build.gradle` file of the Module you wish to profile (`:app` in the example). You will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -172,7 +172,7 @@ The `AnnotateStreamline` class can now be used in your Kotlin code to add annota
 
 In the example app you could add this in the `onCreate()` function of `MainActivity.kt` after the `Module.load()` call to load the `model.pth`.
 
-This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
+This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the image shown below in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
 
 ![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
 
@@ -180,7 +180,7 @@ This 'colored marker with a string' annotation will add the string and time to S
 
 In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
-To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that if you add annotations in multiple places in your code, the ids are unique.
 
 Here is an example setup in a class's companion object:
 
@@ -199,9 +199,9 @@ Here is an example setup in a class's companion object:
     }
 ```
 
-If you're using the example app, add this to the `MainActivity` class.
+For the example app, add this to the `MainActivity` class.
 
-Then they can be used like this:
+Then it can be used like this:
 
 ```kotlin
       val preprocess = currentId++
@@ -214,7 +214,7 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-In the example app the CAM annotations would be added to the `runInference()` function, which would end up looking like this:
+In the example app, the CAM annotations are added to the `runInference()` function, which should look like this:
 
 ```kotlin
     private fun runInference(bitmap: Bitmap) {
@@ -244,6 +244,6 @@ In the example app the CAM annotations would be added to the `runInference()` fu
     }
 ```
 
-The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
+The example application is very fast and simple, so the CAMs will not show much information. In a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. For this example app with its very fast inference, it's best to change the Streamline timeline view scale to 10µs in order to see the CAM annotations better.
 
-Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
+Once you've added in useful CAM annotations, you can build and deploy a debug version of your application. You can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. After the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.

From 0c8b302bbe418985a1add2a9a05f8dcf475b2000 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:17:17 -0500
Subject: [PATCH 26/87] Update nn-profiling-general.md

---
 .../profiling-ml-on-arm/nn-profiling-general.md             | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index 7ca228759f..91a35381f1 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -9,8 +9,8 @@ layout: learningpathall
 ## Profiling your model
 App profilers will give you a good overall view of your performance, but often you might want to look inside the model and work out bottlenecks within the network. The network is often the bulk of the time, in which case it will warrant closer analysis. 
 
-General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
+With general profilers this is hard to do, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier to use tools from a framework or inference engine that already has the required instrumentation.
 
-Depending on your model the tools available will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run the model with on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+Depending on your model, your choice of tools will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run with the model running on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings among other useful information.
 
-If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.
+If you are using PyTorch, you will probably use ExecuTorch the ons-device inference runtime for your Android phone. ExecuTorch has a profiler available alongside it.

From 09d1b819a1cffe3184f16cb379080804e75fcdf2 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:45:38 -0500
Subject: [PATCH 27/87] Update nn-profiling-executenetwork.md

---
 .../nn-profiling-executenetwork.md            | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index b046364a82..f4ca26994d 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -1,5 +1,5 @@
 ---
-title: ML profiling with ExecuteNetwork
+title: ML profiling of a tflite model with ExecuteNetwork
 weight: 6
 
 ### FIXED, DO NOT MODIFY
@@ -7,39 +7,38 @@ layout: learningpathall
 ---
 
 ## ArmNN's Network Profiler
-One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of the app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to spot any obvious problems.
 
-If you want to have a tflite to practice this Learning Path with, you can download one from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). As an example we'll use a [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
+To try this out, you can download a tflite model from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). In this Learning Path, you will download [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
 
-To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
+To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the `tar.gz` archive that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
-To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
+To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like `\<user>\AppData\Local\Android\Sdk\platform-tools`. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
 
-Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
+Unzip the `tar.gz` folder you downloaded. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ # if you are using the example tflite model
-# adb push yourmodel.tflite /data/local/tmp/ # where yourmodel is the tflite NN model file that you wish to profile
+adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ 
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
 adb push libarmnn_support_library.so /data/local/tmp/
 # more ArmNN .so library files
 ```
-Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+Push all the `.so` library files that are in the base folder of the `tar.gz` archive you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
 
 Then you need to set the permissions on the files:
 
 ```bash
-adb shell        # to get into the adb shell on the phone
+adb shell       
 cd /data/local/tmp
-chmod 777 ExecuteNetwork    # to make the file executable
-chmod 777 *.so	   # to make the library files executable
+chmod 777 ExecuteNetwork    
+chmod 777 *.so	   
 ```
 
-Then you can run ExecuteNetwork to profile the model. If you are using the example tflite, you can use the following command:
+Now you can run ExecuteNetwork to profile the model. With the example tflite, you can use the following command:
 
 ```bash
 LD_LIBRARY_PATH=. ./ExecuteNetwork -m mobilenet_v2_1.0_224_INT8.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
@@ -49,12 +48,12 @@ If you are using your own tflite, replace `mobilenet_v2_1.0_224_INT8.tflite` wit
 
 This will run the model twice, outputting the layer timings to `modelout.txt`. The `--iterations 2` flag is the command that means it runs twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
 
-For the other flags, of note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
+The other flags to note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input it is meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some math optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
 
-After running the model, you can pull the output file back to your computer with the following commands:
+After running the model, you can pull the output file back to your host machine with the following commands:
 
 ```bash
-exit         # to leave the adb shell
+exit        
 adb pull /data/local/tmp/modelout.txt
 ```
 Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
@@ -62,6 +61,7 @@ Once again, this can be done with drag and drop in Android Studio's *Device Expl
 Depending on the size of your model, the output will probably be quite large. You can use a text editor to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
 
 At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
+
 ```text
 Info: ArmNN v33.2.0
 Info: Initialization time: 7.20 ms.
@@ -78,8 +78,8 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  
+After the summary comes the graph of the model, then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself. You can skip past the graph and the optimization timings to get to the part that needs analyzing.  
 
-In the mobilenet example output, the graph is from lines 18 to 1629. After this come the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that wants analyzing starts.
+In the mobilenet example output, the graph is from lines 18 to 1629. After this is the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that needs analyzing starts.
 
-The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
\ No newline at end of file
+The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.

From 73461abce4dcab18f35759b6ca283726b6880697 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:57:55 -0500
Subject: [PATCH 28/87] Update app-profiling-android-studio.md

---
 .../app-profiling-android-studio.md                    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 7d49a7303b..9f8508f3a8 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -13,7 +13,7 @@ To find the Profiler, open your project in Android Studio and click on the *View
 
 Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if, for example, you're using LiteRT (formerly known as TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
 
-Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
+Before you start either task, you have to build your app for profiling. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). You will want to start the correct profiling version of the app depending on the task.
 
 ![Android Studio profiling run types alt-text#center](android-profiling-version.png "Figure 1. Profiling run versions")
 
@@ -21,14 +21,14 @@ For the Java/Kotlin side, you want the **debuggable** "Profile 'app' with comple
 
 ### Java/Kotlin
 
-If we start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then we can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
+If you start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then you can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
 
 ![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
 
-When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
+When you're ready, *Stop* the profiling again. Now there will be a nice timeline graph of memory usage. While Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
 ![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
 
-The default height of the Profiling view - as well as the timeline graph within it - is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+The default height of the Profiling view, as well as the timeline graph within it is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
 
 Looking further down you can see the *Table* of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
 
@@ -36,7 +36,7 @@ Looking further down you can see the *Table* of Java/Kotlin allocations for your
 
 For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you have to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
 
-The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating significant memory. Potentially more useful, you can also see Remaining Size. 
 
 In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
 

From 46c451bbc9543de98759ecd8db8d87a690217a31 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 16:03:49 -0500
Subject: [PATCH 29/87] Update _review.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/_review.md     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index a1f46d1ed9..7eae5a8b1b 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -16,8 +16,8 @@ review:
         question: >
             Does Android Studio have a profiler?
         answers:
-            - Yes
-            - No
+            - "Yes"
+            - "No"
         correct_answer: 1                   
         explanation: >
             Yes, Android Studio has a built-in profiler that can be used to monitor the memory usage of your app among other things

From fc1a47d07e157058499d528eb6fa85ca267708bd Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 16:07:53 -0500
Subject: [PATCH 30/87] Update _next-steps.md

---
 .../profiling-ml-on-arm/_next-steps.md                       | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
index aafea4126a..f468cb1b80 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -1,8 +1,7 @@
 ---
-next_step_guidance: Continue Learning
+next_step_guidance: You might be interested in learning how to profile your Unity apps on Android.
 
-recommended_path: /learning-paths/smartphones-and-mobile/build-llama3-chat-android-app-using-executorch-and-xnnpack/
-recommended_path: /learning-paths/smartphones-and-mobile/kleidiai-on-android-with-mediapipe-and-xnnpack/
+recommended_path: /learning-paths/smartphones-and-mobile/profiling-unity-apps-on-android/
 
 further_reading:
     - resource:

From acf0e266dc8128225f46a171d792c875a3b00198 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Thu, 14 Nov 2024 17:30:31 -0700
Subject: [PATCH 31/87] Update WPA plugin install guide

---
 .../_images/wpa-install-plugin.png            | Bin 0 -> 89367 bytes
 .../_images/wpa-installation.png              | Bin 68266 -> 67889 bytes
 content/install-guides/_images/wpa-store.png  | Bin 0 -> 127218 bytes
 .../install-guides/windows-perf-wpa-plugin.md |  62 +++++++++---------
 4 files changed, 31 insertions(+), 31 deletions(-)
 create mode 100644 content/install-guides/_images/wpa-install-plugin.png
 create mode 100644 content/install-guides/_images/wpa-store.png

diff --git a/content/install-guides/_images/wpa-install-plugin.png b/content/install-guides/_images/wpa-install-plugin.png
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diff --git a/content/install-guides/windows-perf-wpa-plugin.md b/content/install-guides/windows-perf-wpa-plugin.md
index 4e0fa1f432..255436c30b 100644
--- a/content/install-guides/windows-perf-wpa-plugin.md
+++ b/content/install-guides/windows-perf-wpa-plugin.md
@@ -1,10 +1,6 @@
 ---
 title: Windows Performance Analyzer (WPA) plugin
 
-draft: true
-cascade:
-    draft: true
-
 minutes_to_complete: 15
 
 official_docs: https://github.com/arm-developer-tools/windowsperf-wpa-plugin
@@ -34,7 +30,7 @@ layout: installtoolsall # DO NOT MODIFY. Always true for tool install articles
 
 The Windows Performance Analyzer (WPA) plugin connects [WindowsPerf](/learning-paths/laptops-and-desktops/windowsperf/) to the Windows Performance Analyzer. Windows Perf is a lightweight performance profiling tool inspired by Linux Perf and designed for Windows on Arm.
 
-Windows Performance Analyzer is a useful tool that supports developers with diagnostics and performance tuning. It generates data tables and graphs of Event Tracing for Windows (ETW) events, which are recorded in one of three ways:
+Windows Performance Analyzer provides developers with diagnostics and performance tuning. It generates data tables and graphs of Event Tracing for Windows (ETW) events, which are recorded in one of three ways:
 - Windows Performance Recorder (WPR)
 - Xperf
 - or through an assessment that's run in the Assessment Platform.
@@ -61,33 +57,35 @@ The telemetry view displays telemetry events grouped by unit:
 
 ## How do I install the WPA plugin?
 
-Before installing the plugin, you need to make sure you have installed WPA:
+Before installing the plugin, you need to install WPA.
 
 ### Install WPA
 
-WPA is included in the Windows Assessment and Deployment Kit (Windows ADK), which you can download from [Microsoft](https://go.microsoft.com/fwlink/?linkid=2243390).
+For Windows on Arm devices, you can install WPA from the Microsoft Store.
 
-{{% notice Note %}}
-The WPA plugin requires WPA version `11.0.7.2` or higher.
-{{% /notice %}}
+Open the Microsoft Store and search for "windows performance analyzer".
 
-Run the downloaded `adksetup.exe` program. 
+![WPA store](/install-guides/_images/wpa-store.png)
 
-Specify the default installation location and accept the license agreement. 
+Hover over the card, and the "Free" button will become a "Get" button. Click the "Get" button to start installation. 
 
-Make sure that **Windows Performance Toolkit** is checked under **Select the features you want to install**.
+Wait for WPA to be installed, and launch it from the Windows menu.
 
-![WPA Installation](/install-guides/_images/wpa-installation.png)
+![WPA installation #center](/install-guides/_images/wpa-installation.png)
 
-Finally, click **Install**.
+{{% notice Note %}}
+The WPA plugin requires WPA version `11.0.7.2` or higher. You can check the version by clicking `Help` -> `About Windows Performance Analyzer`.
+{{% /notice %}}
+
+Close Windows Performance Analyzer.
 
 ### Install the WPA plugin
 
-Now you're ready to install the plugin, which is a single `.dll` file.
+You are ready to install the WPA plugin, which is a single `.dll` file.
 
 Download the `.zip` file from the [Windows Perf WPA plugin GitHub releases page](https://github.com/arm-developer-tools/windowsperf-wpa-plugin/releases) on GitHub.
 
-Alternatively, you can download the latest version using command prompt:
+Alternatively, you can download the `.zip` file from a Command Prompt:
 
 ```console
 mkdir wpa-plugin
@@ -95,7 +93,7 @@ cd wpa-plugin
 curl -L -O https://github.com/arm-developer-tools/windowsperf-wpa-plugin/releases/download/1.0.2/wpa-plugin-1.0.2.zip
 ```
 
-Now extract the `.dll` file from the downloaded `.zip` file. 
+Extract the `.dll` file from the downloaded `.zip` file. 
 
 ```console
 tar -xmf wpa-plugin-1.0.2.zip
@@ -103,30 +101,32 @@ tar -xmf wpa-plugin-1.0.2.zip
 
 The file `WPAPlugin.dll` is now in your `wpa-plugin` directory. 
 
-There are three ways you can install the `WPAPlugin.dll` file: 
+There are three ways you can use the `WPAPlugin.dll` file: 
 
-#### 1. Copy the .dll file to the CustomDataSources directory next to the WPA executable.
+#### 1. Start WPA from the command line and pass the plugin directory location using a flag.
 
-The default location is: 
-        `C:\\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit\CustomDataSources`
+Use the `-addsearchdir` flag to tell `wpa` where to find plugins.
+
+For example, if you downloaded the DLL in your `Downloads` directory, you can run `wpa` as shown below:
 
+```bash
+wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2
+```
+        
 #### 2. Set an environment variable.
 
 Set the `WPA_ADDITIONAL_SEARCH_DIRECTORIES` environment variable to the location of the `.dll` file.
 
-#### 3. Start WPA from the command line and pass the plugin directory location using a flag.
+#### 3. Copy the .dll file to the CustomDataSources directory next to the WPA executable.
 
-Use the `-addsearchdir` flag for `wpa`:
+The default location is: 
+        `C:\\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit\CustomDataSources`
 
-```bash
-wpa -addsearchdir "%USERPROFILE%\plugins"
-```
-        
 ## How can I verify the WPA plugin is installed?
 
-To verify the plugin is loaded, launch WPA and the plugin should appear under **Help > About Windows Performance Analyzer**.
+To verify the plugin is loaded, launch WPA and the plugin should appear on the Installed Plugins list.
 
-![WPA installation confirmation](/install-guides/_images/about-wpa.png)
+![WPA installation confirmation](/install-guides/_images/wpa-install-plugin.png)
 
 ## How can I run the WPA plugin from the command line?
 
@@ -135,7 +135,7 @@ To open a JSON file directly from the command line, you can use the `-i` flag to
 For example: to open `timeline_long.json` in your downloads directory, run the command:
 
 ```console
-wpa -i "%USERPROFILE%\\Downloads\\timeline_long.json"
+wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2 -i %USERPROFILE%\Downloads\timeline_long.json
 ```
 ## How do I uninstall the WPA plugin?
 

From 42cf265e22422e38693035eaf0b1b73fafa52e28 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Fri, 15 Nov 2024 17:29:28 +0100
Subject: [PATCH 32/87] Update YOLOv8 on Himax Learning Path

---
 .../microcontrollers/yolo-on-himax/_index.md  | 32 ++++---
 .../microcontrollers/yolo-on-himax/_review.md | 24 +++++-
 .../yolo-on-himax/how-to-1.md                 | 85 ++++++++++++-------
 .../yolo-on-himax/how-to-2.md                 | 42 +++++----
 .../yolo-on-himax/how-to-3.md                 | 34 ++++----
 .../yolo-on-himax/how-to-4.md                 | 27 ++++--
 .../yolo-on-himax/how-to-5.md                 | 26 +++---
 7 files changed, 163 insertions(+), 107 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 59d523d0b6..4a5167d254 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -3,33 +3,37 @@ title: Run a Computer Vision Model on a Himax Microcontroller
 
 minutes_to_complete: 90
 
-who_is_this_for: This is an introduction topic for beginners on how to run a computervision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
+who_is_this_for: This is an introduction topic for beginners on how to run a computer vision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
+
+learning_objectives:
+    - Run a you-only-look-once (YOLO) computer vision model on the edge device
+    - Build the Himax Software Development Kit (SDK) and generate the firmware image file
+    - Update the firmware on the edge device (Himax WiseEye2)
 
-learning_objectives: 
-    - Run a you-only-look-once (YOLO) computer vision model using off-the-shelf hardware based on the Arm Cortex-M55 and Ethos-U55.
-    - Learn how to build the Himax SDK and generate firmware image file.
-    - Learn how to update firmware on edge device (Himax WiseEye2).
-    
 prerequisites:
-    - Seeed Grove Vision AI V2 Module
-    - OV5647-62 Camera module and included FPC cable
+    - A [Seeed Grove Vision AI Module V2](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html) development board
+    - A [OV5647-62 Camera Module](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html) and included FPC cable
     - A USB-C cable
-    - A Linux/Windows-based PC on an x86 archiecture. 
+    - An x86 based Linux machine or a machine running Apple Silicon
 
 author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
 ### Tags
-skilllevels: Beginner
+skilllevels: Introductory
 subjects: ML
 armips:
-    - Cortex M55
-    - Ethos U55
+    - Cortex-M55
+    - Ethos-U55
 tools_software_languages:
     - Himax SDK
-    - Bash
+    - Python
 operatingsystems:
     - Linux
-    - Windows
+    - macOS
+
+draft: true
+cascade:
+    draft: true
 
 
 ### FIXED, DO NOT MODIFY
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
index 27a46683f1..98489df86d 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
@@ -2,13 +2,31 @@
 review:
     - questions:
         question: >
-            The Grove Vision AI V2 Module can run Yolov8 model in real time?
+            The Grove Vision AI V2 Module can run YOLOv8 model in real time
         answers:
             - True
             - False
-        correct_answer: 1                    
+        correct_answer: 1
         explanation: >
-            The Grove Vision AI V2 Module can run object detection in real time using the Cortex-M55 and Ethos-U55. 
+            The Grove Vision AI V2 Module can run object detection in real time thanks to it's ML accelerated capabilites.
+        question: >
+            Which of the options is the YOLO model unable to run?
+        answers:
+            - Pose detection
+            - Object detection
+            - Speech-to-text transcription
+        correct_answer: 3
+        explanation: >
+            The YOLO model is a computer vision model, meaning it runs based on images as input.
+        question: >
+            What Arm IP on the Seeed Grove Vision AI Module V2 enables you to run ML workloads efficiently?
+        answers:
+            - Ethos-U55
+            - Cortex-A72
+            - Cortex-X4
+        correct_answer: 1
+        explanation: >
+            When paired with the low-power Cortex-M55 processor, the Ethos-U55 provides an uplift in ML performance
 
 
 # ================================================================================
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index da46268655..7af1eb6ceb 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -1,46 +1,32 @@
 ---
-title: Set Up Environment
+title: Set up environment
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Set up the Development Environment
+# Set up the development environment
 
-### Step 1.1. Install Ubuntu
+This learning path has been validated on Ubuntu 22.04 LTS and macOS.
 
-If you are running Windows on your host machine, we recommend using Ubuntu through Windows subsystem for Linux 2 (WSL2). Please see [this learning path](https://learn.arm.com/learning-paths/laptops-and-desktops/wsl2/setup/) for assistance  
+{{% notice %}}
+If you are running Windows on your host machine, you can use Ubuntu through Windows subsystem for Linux 2 (WSL2). Check out [this learning path](https://learn.arm.com/learning-paths/laptops-and-desktops/wsl2/setup/) to get started.
+{{% /notice %}}
 
-This learning path has been validated on Ubuntu 22.04 LTS. However, we expect other linux distributions to work. To verify the Linux distribution you are using you can run the `cat /etc/*release*` command. 
+## Install Python, pip and git
 
+You will use Python to build the firmware image and pip to install some dependencies. Verify Python is installed by running
 ```bash
-cat /etc/*release*
-```
-The top lines from the terminal output will show the distribution version. 
-
-```output
-DISTRIB_ID=Ubuntu
-DISTRIB_RELEASE=22.04
-DISTRIB_CODENAME=jammy
-DISTRIB_DESCRIPTION="Ubuntu 22.04.5 LTS"
-...
+python3 --version
 ```
 
-### Step 1.2. (Optional) Install Microsoft Visual Studio Code
-
-This is only optional. You can use any text editor you are comfortable with to view or edit code. By typing “wsl” in VS Code terminal, you can switch to Linux environment.
-
-### Step 1.3. Install python 3
-
-Go to website python.org to download and install.
-Verify python is installed by
-python3 --version
 You should see an output like the following.
 ```output
 Python 3.12.7
 ```
-### Step 1.4. Install python-pip
+
+Install `pip` with the following commands, and check the output to verify it's installed correctly.
 
 ```bash
 sudo apt update
@@ -48,21 +34,39 @@ sudo apt install python3-pip -y
 pip3 --version
 ```
 
-If `pip3` is correctly installed you should see an output similar to tht following.
+```output
+pip 24.2 from /<path-to>/pip (python 3.12)
+```
+
+You will need to have the git version control system installed. Run the command below to verify that git is installed on your system.
+
+```bash
+git --version
+```
+
+You should see output similar to that below.
 
 ```output
-pip 24.2 from <path to pip3>/pip (python 3.12)
+git version 2.39.3
 ```
 
-### Step 1.5. Install make
+## Install make
+
+Install the make build tool, which is used to build the firmware in the next section.
 
-You will need to install the make build tool in order to build the firmware in the following section.
+### Linux
 
 ```bash
 sudo apt update
 sudo apt install make -y
 ```
 
+### macOS
+
+```console
+brew install make
+```
+
 Successful installation of make will show the following when the `make --version` command is run.
 
 ```output
@@ -74,15 +78,32 @@ License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
 This is free software: you are free to change and redistribute it.
 There is NO WARRANTY, to the extent permitted by law.
 ```
+{{% notice Note %}}
+To run this learning path on macOS, you need to verify that your installation is for the GNU Make - not the BSD version.
+{{% /notice %}}
+## Install Arm GNU toolchain
+
+### Linux
 
-### Step 1.6. Install ARM GNU toolchain
+The toolchain is used to cross-compile from the host architecture (x86) to the embedded device architecture (AArch64).
 
 ```bash
-cd ~
+cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 export PATH="$HOME/arm-gnu-toolchain-13.2.Rel1-x86_64-arm-none-eabi/bin/:$PATH"
 ```
+### macOS
+```console
+cd $HOME
+wget https://developer.arm.com/-/media/Files/downloads/gnu/13.3.rel1/binrel/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
+tar -xvf arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
+export PATH="$HOME/code/tmp/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi/bin/:$PATH"
+```
+
+{{% notice %}}
+You can add the above command to the `.bashrc` file. This was, the Arm GNU toolchain is configured from new terminal sessions as well.
+{{% /notice %}}
 
-Please note: you may want to add the command to your `bashrc` file. This enables the Arm GNU toolchain to be easily accessed from any new terminal session. 
 
+Now that your development environment is set up, move on to the next section where you will generate the firmware image.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index 388c390f06..714961dc13 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -1,39 +1,25 @@
 ---
-title: Build The Firmware
+title: Build the firmware
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Build The Firmware
+TODO short intro / framing?
 
-Next, we need to build an image that contains the embedded software (firmware). You will need to have the git version control system installed. Run the command below to verify that git is installed on your system.
+## Clone the Himax project
 
-```bash
-git --version
-```
-
-You should see output similar to that below.
-
-```output
-git version 2.39.3
-```
-
-If not, please follow the steps to install git on your system.
-
-### Step 2.1. Clone the Himax project
-
-You will first need to recusively clone the Himax repository. This will also clone the necessary sub repos such as Arm CMSIS. 
+Himax has set up a repository containing a few examples for the Seeed Grove Vision AI V2 board. By recursively cloning the Himax examples repo, git will include the necessary sub-repositories that have been configured for the project.
 
 ```bash
 git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git
 cd Seeed_Grove_Vision_AI_Module_V2
 ```
 
-### Step 2.2. Compile the Firmware
+## Compile the firmware
 
-The make build tool is used to compile the source code. This should take up around 2-3 minutes depending on the number of CPU cores available.
+The make build tool is used to compile the source code. This should take up to 10 minutes depending on the number of CPU cores available.
 
 ```bash
 cd EPII_CM55M_APP_S
@@ -41,20 +27,32 @@ make clean
 make
 ```
 
+## Generate the firmware image
 
-### Step 2.3. Generate a Firmware Image
+The examples repository contains scripts to generate the image.
 
 ```bash
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
+```
+
+## Linux
+
+```bash
 ./we2_local_image_gen project_case1_blp_wlcsp.json
 ```
 
-Your terminal output should end with the following.
+## macOS
+```console
+./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
+```
 
+Your terminal output should end with the following.
 ```output
 Output image: output_case1_sec_wlcsp/output.img
 Output image: output_case1_sec_wlcsp/output.img
 
 IMAGE GEN DONE
 ```
+
+With this step, you are ready to flash the image onto the Himax development board.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index b8fde69fd0..06d110108b 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -1,46 +1,48 @@
 ---
-title: Flash Firmware onto the Microcontroller
+title: Flash firmware onto the microcontroller
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Flash the Firmware
+Now that we have generated a firmware file on our local machine, we need to flash the microcontroller with this firmware.
 
-Now that we have generated a firmware file on our local machine, we need to flash the microcontroller with this firmware. 
+## Install xmodem
 
-### Step 3.1. Install xmodem.
-
-`Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. 
+`Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. If you cloned the repository to a different location replace $HOME with the path.
 
 ```bash
-cd $HOME/Seeed_Grove_Vision_AI_Module_V2 # If you cloned the repo to a different location replace $HOME with the path. 
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2
 pip install -r xmodem/requirements.txt
 ```
 
-### Step 3.2. Connect the module to PC by USB cable.
+## Connect the module
 
-You will need to insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
+Insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
 
 ![unlatched](./unlatched.jpg)
 
-Then, slide the FPC connector in with the metal pins facing down and close the dark grey latch to fasten the connector. 
+Then, slide the FPC connector in with the metal pins facing down and close the dark grey latch to fasten the connector.
 
 ![latched](./latched.jpg)
 
 Then connect the Groove Vision AI V2 Module to your computer via the USB-C cable.
 
-### Step 3.4. Flash the firmware onto the moule.
+### Flash the firmware onto the module
 
-Run the python script below to flash the firmware. 
+Run the python script below to flash the firmware.
 
-```python
-python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img 
+```bash
+python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
 ```
 
-    Note: If running one of the other example models demonstrated in '(Optional) Try Different Models', the command might be slightly different.
+{{% notice Note %}}
+When you run other example models demonstrated in the later section [Object detection and additional models](/learning-paths/microcontrollers/yolo-on-himax/how-to-5/), you need to adapt this command.
+{{% /notice %}}
+
+TODO: how will the command really change? How to find COM number?
 
-After the firmware image burning is completed, the message "Do you want to end file transmission and reboot system? (y)" is displayed. Press the reset button on the module as per the image below.
+After the firmware image burning is completed, the message `Do you want to end file transmission and reboot system? (y)` is displayed. Press the reset button indicated in the image below.
 
 ![reset button](./reset_button.jpg)
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index 2aa5632762..522f23f29b 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -1,5 +1,5 @@
 ---
-title: Run and View Model Results
+title: Run and view model results
 weight: 3
 
 ### FIXED, DO NOT MODIFY
@@ -7,20 +7,29 @@ layout: learningpathall
 ---
 
 
-### Step 4.1. Connect module to PC with USB cable.
+## Connect the board with USB cable
 
-Exit the terminal session and connect the module to the PC via your USB-C cable. 
+Exit the terminal session and connect the module to your host machine using the USB-C cable.
 
-### Step 4.2. Download the Himax AI web toolkit.  
+## Download the Himax AI web toolkit
 
-The Himax AI web toolkit enables a browser-based graphical user interface (GUI) for the live camera feed. 
+The Himax AI web toolkit enables a browser-based graphical user interface (GUI) for the live camera feed.
 
-Download the Himax AI Web toolkit by clicking on this [link](https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip)
+```bash
+wget https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip
+unzip Himax_AI_web_toolkit.zip
+```
 
-Unzip the archived file and double click `index.html`. This will open the GUI within your default browser.
+Open the unzipped directory in your file browsing system and double click `index.html`. This will open the GUI within your default browser.
 
-### Step 4.3. Connect to the Grove Vision AI
+## Connect to the Grove Vision AI
 
-Select 'Grove Vision AI(V2)' in the top-right hand corner and press connect button. 
+Select `Grove Vision AI(V2)` in the top-right hand corner and press `Connect` button. Follow the instructions to set up the connection.
 
 ![Himax web UI](./himax_web_ui.jpg)
+
+The image will run the YOLOv8 on your device. By using the camera to identify things from the [Common Objects in Context (COCO) dataset](https://cocodataset.org/#home), which the model has been trained on, you can put the it to the test. Get some common objects ready and move on to the next section.
+
+## View model results
+
+TODO have running section here?
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
index cb6ad788f2..9f3cfde92d 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
@@ -1,21 +1,25 @@
 ---
-title: (Optional) Try Different Models
+title: Object detection and additional models
 weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
+TODO some more intro here, and showing how to test the model
+Also double check this section
 
-### Modify the makefile
+## Modify the Makefile
 
-Change the directory to the where the makefile is located.
+TODO: why are we doing this?
+
+Change the directory to the where the Makefile is located. If you cloned the repository to a different location, replace $HOME with the path.
 
 ```bash
-cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/ # replace $HOME with the location of the project
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
 ```
 
-Using a text editor, for example visual studio code or nano, modify the `APP_TYPE` field in the makefile from the default value of `allon_sensor_tflm` to one of the values in the table below 
+Modify the `APP_TYPE` field in the Makefile from the default value of `allon_sensor_tflm` to one of the values in the table below
 
 
 |APP_TYPE =|Description|
@@ -24,18 +28,18 @@ Using a text editor, for example visual studio code or nano, modify the `APP_TYP
 |tflm_folov8_pose|Pose detection|
 |tflm_fd_fm|Face detection|
 
-### Regenerate the Firmware Image
+## Regenerate the firmware image
 
-Go back to the 'Build The Firmware' section and start from Step 3.2. to regenerate the firmware image. 
+Go back to the [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/how-to-3/) section and run the python command to regenerate the firmware image.
 
-The images below are examples images from the model. 
+The images below are examples images from the model.
 
-#### Objection Detection
+### Objection detection
 ![object_detection](./object_detection.jpg)
 
-#### Pose Estimation
+### Pose estimation
 ![Pose estimation](./pose_estimation.jpg)
 
-#### Face Detection
+### Face detection
 ![object_detection](./face_detection.jpg)
 

From 9a9b50dbc56fe2573146f297c99ab610fb222b79 Mon Sep 17 00:00:00 2001
From: GitHub Actions Stats Bot <>
Date: Mon, 18 Nov 2024 01:28:54 +0000
Subject: [PATCH 33/87] automatic update of stats files

---
 data/stats_current_test_info.yml |  2 +-
 data/stats_weekly_data.yml       | 84 ++++++++++++++++++++++++++++++++
 2 files changed, 85 insertions(+), 1 deletion(-)

diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml
index ecd46e69b0..1eca8f7819 100644
--- a/data/stats_current_test_info.yml
+++ b/data/stats_current_test_info.yml
@@ -1,5 +1,5 @@
 summary:
-  content_total: 301
+  content_total: 303
   content_with_all_tests_passing: 32
   content_with_tests_enabled: 34
 sw_categories:
diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml
index 4844d39087..d47c32756f 100644
--- a/data/stats_weekly_data.yml
+++ b/data/stats_weekly_data.yml
@@ -3834,3 +3834,87 @@
     avg_close_time_hrs: 0
     num_issues: 17
     percent_closed_vs_total: 0.0
+- a_date: '2024-11-18'
+  content:
+    cross-platform: 25
+    embedded-systems: 19
+    install-guides: 89
+    laptops-and-desktops: 32
+    microcontrollers: 25
+    servers-and-cloud-computing: 88
+    smartphones-and-mobile: 25
+    total: 303
+  contributions:
+    external: 43
+    internal: 353
+  github_engagement:
+    num_forks: 30
+    num_prs: 8
+  individual_authors:
+    alaaeddine-chakroun: 1
+    alexandros-lamprineas: 1
+    annie-tallund: 1
+    arm: 3
+    arnaud-de-grandmaison: 1
+    basma-el-gaabouri: 1
+    ben-clark: 1
+    bolt-liu: 2
+    brenda-strech: 1
+    chaodong-gong,-alex-su,-kieran-hejmadi: 1
+    chen-zhang: 1
+    christopher-seidl: 7
+    cyril-rohr: 1
+    daniel-gubay: 1
+    daniel-nguyen: 1
+    david-spickett: 2
+    dawid-borycki: 30
+    diego-russo: 1
+    diego-russo-and-leandro-nunes: 1
+    elham-harirpoush: 2
+    florent-lebeau: 5
+    "fr\xE9d\xE9ric--lefred--descamps": 2
+    gabriel-peterson: 5
+    gayathri-narayana-yegna-narayanan: 1
+    graham-woodward: 1
+    iago-calvo-lista,-arm: 1
+    james-whitaker,-arm: 1
+    jason-andrews: 89
+    joe-stech: 1
+    johanna-skinnider: 2
+    jonathan-davies: 2
+    jose-emilio-munoz-lopez,-arm: 1
+    julie-gaskin: 4
+    julio-suarez: 5
+    kasper-mecklenburg: 1
+    koki-mitsunami: 1
+    konstantinos-margaritis: 7
+    kristof-beyls: 1
+    liliya-wu: 1
+    mathias-brossard: 1
+    michael-hall: 5
+    nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1
+    odin-shen: 1
+    owen-wu,-arm: 2
+    pareena-verma: 35
+    pareena-verma,-annie-tallund: 1
+    pareena-verma,-jason-andrews,-and-zach-lasiuk: 1
+    pareena-verma,-joe-stech,-adnan-alsinan: 1
+    pranay-bakre: 4
+    przemyslaw-wirkus: 1
+    rin-dobrescu: 1
+    roberto-lopez-mendez: 2
+    ronan-synnott: 45
+    thirdai: 1
+    tianyu-li: 1
+    tom-pilar: 1
+    uma-ramalingam: 1
+    varun-chari,-pareena-verma: 1
+    visualsilicon: 1
+    ying-yu: 1
+    ying-yu,-arm: 1
+    zach-lasiuk: 1
+    zhengjun-xing: 2
+  issues:
+    avg_close_time_hrs: 0
+    num_issues: 12
+    percent_closed_vs_total: 0.0

From e4a24c4571fc6ded8935f150fd395ded6f46aeab Mon Sep 17 00:00:00 2001
From: Odin Shen Coder <odin.code.shen@gmail.com>
Date: Mon, 18 Nov 2024 13:06:43 +0000
Subject: [PATCH 34/87] Correct weight on Himax yolo LP

---
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-3.md   | 2 +-
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-4.md   | 2 +-
 .../learning-paths/microcontrollers/yolo-on-himax/how-to-5.md   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index b8fde69fd0..7c0cdd05a7 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -1,6 +1,6 @@
 ---
 title: Flash Firmware onto the Microcontroller
-weight: 3
+weight: 4
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index 2aa5632762..6477442f3c 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -1,6 +1,6 @@
 ---
 title: Run and View Model Results
-weight: 3
+weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
index cb6ad788f2..367cbe1766 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
@@ -1,6 +1,6 @@
 ---
 title: (Optional) Try Different Models
-weight: 5
+weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

From cae073faf243d6ce2232809158336c24241597a5 Mon Sep 17 00:00:00 2001
From: Odin Shen Coder <odin.code.shen@gmail.com>
Date: Mon, 18 Nov 2024 15:32:19 +0000
Subject: [PATCH 35/87] Add Grove Vision AI V2 Module link and add one
 question.

---
 .../microcontrollers/yolo-on-himax/_index.md          |  2 +-
 .../microcontrollers/yolo-on-himax/_review.md         | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 59d523d0b6..eb7fd79dcc 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -11,7 +11,7 @@ learning_objectives:
     - Learn how to update firmware on edge device (Himax WiseEye2).
     
 prerequisites:
-    - Seeed Grove Vision AI V2 Module
+    - Seeed Studio [Grove Vision AI V2 Module](https://wiki.seeedstudio.com/grove_vision_ai_v2/) 
     - OV5647-62 Camera module and included FPC cable
     - A USB-C cable
     - A Linux/Windows-based PC on an x86 archiecture. 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
index 27a46683f1..8049916a7e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
@@ -10,6 +10,17 @@ review:
         explanation: >
             The Grove Vision AI V2 Module can run object detection in real time using the Cortex-M55 and Ethos-U55. 
 
+    - questions:
+        question: >
+            What the IP the Grove Vision AI V2 Module integrated?
+        answers:
+            - Cortex-M55
+            - Ethous-U55
+            - Both Cortex-M55 and Ethous-U55 
+        correct_answer: 3                    
+        explanation: >
+            The Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
+
 
 # ================================================================================
 #       FIXED, DO NOT MODIFY

From 4f062367b898cd84b6a3fb18b2ad8586a0c0a734 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Wed, 20 Nov 2024 17:10:24 +0000
Subject: [PATCH 36/87] Editorial review of update.

---
 .../install-guides/windows-perf-wpa-plugin.md | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/content/install-guides/windows-perf-wpa-plugin.md b/content/install-guides/windows-perf-wpa-plugin.md
index 255436c30b..59196a27a0 100644
--- a/content/install-guides/windows-perf-wpa-plugin.md
+++ b/content/install-guides/windows-perf-wpa-plugin.md
@@ -31,17 +31,17 @@ layout: installtoolsall # DO NOT MODIFY. Always true for tool install articles
 The Windows Performance Analyzer (WPA) plugin connects [WindowsPerf](/learning-paths/laptops-and-desktops/windowsperf/) to the Windows Performance Analyzer. Windows Perf is a lightweight performance profiling tool inspired by Linux Perf and designed for Windows on Arm.
 
 Windows Performance Analyzer provides developers with diagnostics and performance tuning. It generates data tables and graphs of Event Tracing for Windows (ETW) events, which are recorded in one of three ways:
-- Windows Performance Recorder (WPR)
-- Xperf
-- or through an assessment that's run in the Assessment Platform.
+- By using Windows Performance Recorder (WPR).
+- By using Xperf.
+- Through an assessment that is run in the Assessment Platform.
  
-WPA can open event trace log (ETL) files, which you can use for analysis.
+WPA can open Event Trace Log (ETL) files, which you can use for analysis.
 
-The WPA plugin is built using the [Microsoft Performance Toolkit SDK](https://github.com/microsoft/microsoft-performance-toolkit-sdk), a collection of tools to create and extend performance analysis applications. The plugin parses JSON output from Windows Perf so that it can be visualized in WPA. 
+The WPA plugin is built using the [Microsoft Performance Toolkit SDK](https://github.com/microsoft/microsoft-performance-toolkit-sdk), which is a collection of tools to create and extend performance analysis applications. The plugin parses JSON output from Windows Perf so that it can be visualized in WPA. 
 
-## What are some of the features of the WPA plugin? 
+## What are the features of the WPA plugin? 
 
-The WindowsPerf GUI extension includes features, which are designed to streamline the user experience:
+The WindowsPerf GUI extension includes features that are designed to streamline the user experience, and these are detailed below.
 
 ### Timeline view
 
@@ -67,25 +67,25 @@ Open the Microsoft Store and search for "windows performance analyzer".
 
 ![WPA store](/install-guides/_images/wpa-store.png)
 
-Hover over the card, and the "Free" button will become a "Get" button. Click the "Get" button to start installation. 
+Hover over the card, and you will see that the **Free** button becomes a **Get** button. Click the **Get** button to start the installation. 
 
-Wait for WPA to be installed, and launch it from the Windows menu.
+Wait for WPA to be installed, and then launch it from the Windows menu.
 
 ![WPA installation #center](/install-guides/_images/wpa-installation.png)
 
 {{% notice Note %}}
-The WPA plugin requires WPA version `11.0.7.2` or higher. You can check the version by clicking `Help` -> `About Windows Performance Analyzer`.
+The WPA plugin requires WPA version `11.0.7.2` or higher. You can check the version by clicking **Help** > **About Windows Performance Analyzer**.
 {{% /notice %}}
 
 Close Windows Performance Analyzer.
 
 ### Install the WPA plugin
 
-You are ready to install the WPA plugin, which is a single `.dll` file.
+You are now ready to install the WPA plugin, which is a single `.dll` file.
 
 Download the `.zip` file from the [Windows Perf WPA plugin GitHub releases page](https://github.com/arm-developer-tools/windowsperf-wpa-plugin/releases) on GitHub.
 
-Alternatively, you can download the `.zip` file from a Command Prompt:
+Alternatively, you can download the `.zip` file from a command prompt:
 
 ```console
 mkdir wpa-plugin
@@ -101,28 +101,28 @@ tar -xmf wpa-plugin-1.0.2.zip
 
 The file `WPAPlugin.dll` is now in your `wpa-plugin` directory. 
 
-There are three ways you can use the `WPAPlugin.dll` file: 
+There are three ways that you can use the `WPAPlugin.dll` file: 
 
-#### 1. Start WPA from the command line and pass the plugin directory location using a flag.
+##### Option 1: Start WPA from the command line and pass the plugin directory location using a flag.
 
 Use the `-addsearchdir` flag to tell `wpa` where to find plugins.
 
-For example, if you downloaded the DLL in your `Downloads` directory, you can run `wpa` as shown below:
+For example, if you downloaded the `.dll` in your `Downloads` directory, you can run `wpa` as shown below:
 
 ```bash
 wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2
 ```
         
-#### 2. Set an environment variable.
+##### Option 2: Set an environment variable.
 
 Set the `WPA_ADDITIONAL_SEARCH_DIRECTORIES` environment variable to the location of the `.dll` file.
 
-#### 3. Copy the .dll file to the CustomDataSources directory next to the WPA executable.
+##### Option 3: Copy the  `.dll` file to the `CustomDataSources` directory next to the WPA executable.
 
 The default location is: 
         `C:\\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit\CustomDataSources`
 
-## How can I verify the WPA plugin is installed?
+## How can I verify that the WPA plugin is installed?
 
 To verify the plugin is loaded, launch WPA and the plugin should appear on the Installed Plugins list.
 
@@ -132,12 +132,12 @@ To verify the plugin is loaded, launch WPA and the plugin should appear on the I
 
 To open a JSON file directly from the command line, you can use the `-i` flag to specify the file path to open.
 
-For example: to open `timeline_long.json` in your downloads directory, run the command:
+For example, to open `timeline_long.json` in your  `downloads` directory, run the command:
 
 ```console
 wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2 -i %USERPROFILE%\Downloads\timeline_long.json
 ```
 ## How do I uninstall the WPA plugin?
 
-To uninstall the plugin simply delete the `WPAPlugin.dll` file.
+To uninstall the plugin, simply delete the `WPAPlugin.dll` file.
 

From 7dcde9a214865803954a72694d51c2283c0335a6 Mon Sep 17 00:00:00 2001
From: GitHub Actions Stats Bot <>
Date: Mon, 25 Nov 2024 01:29:04 +0000
Subject: [PATCH 37/87] automatic update of stats files

---
 data/stats_weekly_data.yml | 84 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)

diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml
index d47c32756f..280ec08538 100644
--- a/data/stats_weekly_data.yml
+++ b/data/stats_weekly_data.yml
@@ -3918,3 +3918,87 @@
     avg_close_time_hrs: 0
     num_issues: 12
     percent_closed_vs_total: 0.0
+- a_date: '2024-11-25'
+  content:
+    cross-platform: 25
+    embedded-systems: 19
+    install-guides: 89
+    laptops-and-desktops: 32
+    microcontrollers: 25
+    servers-and-cloud-computing: 88
+    smartphones-and-mobile: 25
+    total: 303
+  contributions:
+    external: 43
+    internal: 353
+  github_engagement:
+    num_forks: 30
+    num_prs: 9
+  individual_authors:
+    alaaeddine-chakroun: 1
+    alexandros-lamprineas: 1
+    annie-tallund: 1
+    arm: 3
+    arnaud-de-grandmaison: 1
+    basma-el-gaabouri: 1
+    ben-clark: 1
+    bolt-liu: 2
+    brenda-strech: 1
+    chaodong-gong,-alex-su,-kieran-hejmadi: 1
+    chen-zhang: 1
+    christopher-seidl: 7
+    cyril-rohr: 1
+    daniel-gubay: 1
+    daniel-nguyen: 1
+    david-spickett: 2
+    dawid-borycki: 30
+    diego-russo: 1
+    diego-russo-and-leandro-nunes: 1
+    elham-harirpoush: 2
+    florent-lebeau: 5
+    "fr\xE9d\xE9ric--lefred--descamps": 2
+    gabriel-peterson: 5
+    gayathri-narayana-yegna-narayanan: 1
+    graham-woodward: 1
+    iago-calvo-lista,-arm: 1
+    james-whitaker,-arm: 1
+    jason-andrews: 89
+    joe-stech: 1
+    johanna-skinnider: 2
+    jonathan-davies: 2
+    jose-emilio-munoz-lopez,-arm: 1
+    julie-gaskin: 4
+    julio-suarez: 5
+    kasper-mecklenburg: 1
+    koki-mitsunami: 1
+    konstantinos-margaritis: 7
+    kristof-beyls: 1
+    liliya-wu: 1
+    mathias-brossard: 1
+    michael-hall: 5
+    nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1
+    odin-shen: 1
+    owen-wu,-arm: 2
+    pareena-verma: 35
+    pareena-verma,-annie-tallund: 1
+    pareena-verma,-jason-andrews,-and-zach-lasiuk: 1
+    pareena-verma,-joe-stech,-adnan-alsinan: 1
+    pranay-bakre: 4
+    przemyslaw-wirkus: 1
+    rin-dobrescu: 1
+    roberto-lopez-mendez: 2
+    ronan-synnott: 45
+    thirdai: 1
+    tianyu-li: 1
+    tom-pilar: 1
+    uma-ramalingam: 1
+    varun-chari,-pareena-verma: 1
+    visualsilicon: 1
+    ying-yu: 1
+    ying-yu,-arm: 1
+    zach-lasiuk: 1
+    zhengjun-xing: 2
+  issues:
+    avg_close_time_hrs: 0
+    num_issues: 13
+    percent_closed_vs_total: 0.0

From 1b79410746eef75c109bb935d033d28a82a64f76 Mon Sep 17 00:00:00 2001
From: GitHub Actions Stats Bot <>
Date: Mon, 25 Nov 2024 02:34:07 +0000
Subject: [PATCH 38/87] automatic update of stats files

---
 data/stats_weekly_data.yml | 84 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)

diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml
index d47c32756f..280ec08538 100644
--- a/data/stats_weekly_data.yml
+++ b/data/stats_weekly_data.yml
@@ -3918,3 +3918,87 @@
     avg_close_time_hrs: 0
     num_issues: 12
     percent_closed_vs_total: 0.0
+- a_date: '2024-11-25'
+  content:
+    cross-platform: 25
+    embedded-systems: 19
+    install-guides: 89
+    laptops-and-desktops: 32
+    microcontrollers: 25
+    servers-and-cloud-computing: 88
+    smartphones-and-mobile: 25
+    total: 303
+  contributions:
+    external: 43
+    internal: 353
+  github_engagement:
+    num_forks: 30
+    num_prs: 9
+  individual_authors:
+    alaaeddine-chakroun: 1
+    alexandros-lamprineas: 1
+    annie-tallund: 1
+    arm: 3
+    arnaud-de-grandmaison: 1
+    basma-el-gaabouri: 1
+    ben-clark: 1
+    bolt-liu: 2
+    brenda-strech: 1
+    chaodong-gong,-alex-su,-kieran-hejmadi: 1
+    chen-zhang: 1
+    christopher-seidl: 7
+    cyril-rohr: 1
+    daniel-gubay: 1
+    daniel-nguyen: 1
+    david-spickett: 2
+    dawid-borycki: 30
+    diego-russo: 1
+    diego-russo-and-leandro-nunes: 1
+    elham-harirpoush: 2
+    florent-lebeau: 5
+    "fr\xE9d\xE9ric--lefred--descamps": 2
+    gabriel-peterson: 5
+    gayathri-narayana-yegna-narayanan: 1
+    graham-woodward: 1
+    iago-calvo-lista,-arm: 1
+    james-whitaker,-arm: 1
+    jason-andrews: 89
+    joe-stech: 1
+    johanna-skinnider: 2
+    jonathan-davies: 2
+    jose-emilio-munoz-lopez,-arm: 1
+    julie-gaskin: 4
+    julio-suarez: 5
+    kasper-mecklenburg: 1
+    koki-mitsunami: 1
+    konstantinos-margaritis: 7
+    kristof-beyls: 1
+    liliya-wu: 1
+    mathias-brossard: 1
+    michael-hall: 5
+    nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1
+    odin-shen: 1
+    owen-wu,-arm: 2
+    pareena-verma: 35
+    pareena-verma,-annie-tallund: 1
+    pareena-verma,-jason-andrews,-and-zach-lasiuk: 1
+    pareena-verma,-joe-stech,-adnan-alsinan: 1
+    pranay-bakre: 4
+    przemyslaw-wirkus: 1
+    rin-dobrescu: 1
+    roberto-lopez-mendez: 2
+    ronan-synnott: 45
+    thirdai: 1
+    tianyu-li: 1
+    tom-pilar: 1
+    uma-ramalingam: 1
+    varun-chari,-pareena-verma: 1
+    visualsilicon: 1
+    ying-yu: 1
+    ying-yu,-arm: 1
+    zach-lasiuk: 1
+    zhengjun-xing: 2
+  issues:
+    avg_close_time_hrs: 0
+    num_issues: 13
+    percent_closed_vs_total: 0.0

From ee922c1159a4f62c1b7e5a8422fb344c9865f332 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 25 Nov 2024 06:42:47 +0000
Subject: [PATCH 39/87] Editorial of index and intro.

---
 .../cross-platform/simd-info-demo/_index.md    | 18 +++++++++---------
 .../simd-info-demo/intro-to-simdinfo.md        |  8 ++++----
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index cedd43e124..bc2a2cc83a 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -1,30 +1,30 @@
 ---
 title: Introduction to SIMD.info
 
-draft: true
+
 cascade:
-    draft: true
+
 
 minutes_to_complete: 30
 
-who_is_this_for: This is for advanced topic for software developers interested in porting SIMD code across Arm platforms.
+who_is_this_for: This is for software developers interested in porting SIMD code across Arm platforms.
 
 learning_objectives: 
-    - Learn how to use SIMD.info’s tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures and improving code portability.
+    - Describe how to use SIMD.info’s tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures and improving code portability.
 
 prerequisites:
     - A basic understanding of SIMD.
-    - Access to an Arm platform with SIMD supported engine, with recent versions of a C compiler (Clang or GCC) installed.
+    - Access to an Arm platform with a SIMD supported engine, with recent versions of a C compiler such as Clang or GCC installed.
 
-author_primary: Georgios Mermigkis & Konstantinos Margaritis, VectorCamp
+author_primary: Georgios Mermigkis and Konstantinos Margaritis, VectorCamp
 
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
 armips:
-    - Aarch64
-    - Armv8-a
-    - Armv9-a
+    - AArch64
+    - Armv8-A
+    - Armv9-A
 tools_software_languages:
     - GCC
     - Clang
diff --git a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
index 24df6cce42..84466ff1ec 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
@@ -7,10 +7,10 @@ layout: learningpathall
 ---
 
 ### The Challenge of SIMD Code Portability
-One of the biggest challenges developers face when working with SIMD code is making it portable across different platforms. SIMD instructions are designed to increase performance by executing the same operation on multiple data elements in parallel. However, each architecture has its own set of SIMD instructions, making it difficult to write code that works on all of them without major changes to the code and/or algorithm.
+One of the biggest challenges developers face when working with SIMD code is making it portable across different platforms. SIMD instructions are designed to improve performance by executing the same operation on multiple data elements in parallel. Each architecture has its own set of SIMD instructions however, making it difficult to write code that works on all of them without major changes to the code or the algorithm, or both.
 
-To port software written using Intel intrinsics, like SSE/AVX/AVX512, to Arm Neon, you have pay attention to data handling with the different instruction sets.
+For example, to port software written using Intel intrinsics, such as SSE/AVX/AVX512, to Arm Neon, you have pay attention to data handling with the different instruction sets.
 
-Having to port the code between architectures can increase development time and introduce the risk of errors during the porting process. Currently, developers rely on ISA documentation and manually search across various vendor platforms like [Arm Developer](https://developer.arm.com/architectures/instruction-sets/intrinsics/) and [Intel Intrinsics Guide](https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html) to find equivalent instructions.
+Porting the code between architectures can increase development time and introduce the risk of errors. Currently, developers rely on ISA documentation and must manually search across various vendor platforms such as [Arm Developer](https://developer.arm.com/architectures/instruction-sets/intrinsics/) and [Intel Intrinsics Guide](https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html) to find equivalent instructions.
 
-[SIMD.info](https://simd.info) aims to solve this by helping you find equivalent instructions and providing a more streamlined way to adapt your code for different architectures.
+[SIMD.info](https://simd.info) aims to solve this problem by helping you find equivalent instructions and providing a more streamlined way to adapt your code for different architectures.

From a7d92327b464688b9c47f49c7bfb8f27d97bd81b Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 25 Nov 2024 07:05:27 +0000
Subject: [PATCH 40/87] Editorial for description section.

---
 .../simd-info-demo/simdinfo-description.md    | 26 ++++++++++---------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 678d08327c..7c2732794c 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -7,23 +7,25 @@ layout: learningpathall
 ---
 
 ### Comprehensive SIMD.info Capabilities
-**[SIMD.info](https://simd.info/)** offers a variety of powerful tools to help developers work more efficiently with SIMD code across different architectures. With a database of over 10,000 intrinsics, it provides detailed information to support effective SIMD development.
+**[SIMD.info](https://simd.info/)** offers a variety of powerful tools to enable developers to work more efficiently with SIMD code across different architectures. 
 
-For each intrinsic, SIMD.info provides comprehensive details, including:
+With a database of over 10,000 intrinsics, it provides detailed information to support effective SIMD development.
 
-1. **Purpose**: A brief description of what the intrinsic does and its primary use case.
-2. **Result**: Explanation of the output or result of the intrinsic.
-3. **Example**: A code snippet demonstrating how to use the intrinsic.
-4. **Prototypes**: Function prototypes for different programming languages (currently C/C++).
-5. **Assembly Instruction**: The corresponding assembly instruction used by the intrinsic.
-6. **Notes**: Any additional notes or caveats about the intrinsic.
-7. **Architecture**: List of architectures that support the intrinsic
-8. **Link(s) to Official Documentation**
+For each intrinsic, SIMD.info provides the following information:
+
+* Purpose: a brief description of what the intrinsic does and its primary use case.
+* Result: an explanation of the output or result of the intrinsic.
+* Example: a code snippet demonstrating how to use the intrinsic.
+* Prototypes: function prototypes for different programming languages (currently C/C++).
+* Assembly Instruction: the corresponding assembly instruction used by the intrinsic.
+* Notes: any additional notes or caveats about the intrinsic.
+* Architecture: a list of architectures that support the intrinsic.
+* Link(s) to Official Documentation
 
 This detailed information ensures you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
 
-- **Tree-based navigation:** **SIMD.info** uses a clear, hierarchical layout to organize instructions. It categorizes instructions into broad groups like **Arithmetic**, which are further divided into specific subcategories such as **Vector Add** and **Vector Subtract**. This organized structure makes it straightforward to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the exact instructions you need.
-An example of how the tree structure looks like:
+- **Tree-based navigation:** **SIMD.info** uses a clear, hierarchical layout to organize instructions. It categorizes instructions into broad groups like **Arithmetic**, which are further divided into specific subcategories such as **Vector Add** and **Vector Subtract**. This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the exact instructions you need.
+Here is an example of the tree structure:
 
 
     - **Arithmetic** 

From b729d997a7009eedf318a7880c88f03a80e30b43 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 25 Nov 2024 07:09:08 +0000
Subject: [PATCH 41/87] Editorial description.

---
 .../simd-info-demo/simdinfo-description.md             | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 7c2732794c..1b2fb45e12 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -20,9 +20,9 @@ For each intrinsic, SIMD.info provides the following information:
 * Assembly Instruction: the corresponding assembly instruction used by the intrinsic.
 * Notes: any additional notes or caveats about the intrinsic.
 * Architecture: a list of architectures that support the intrinsic.
-* Link(s) to Official Documentation
+* Link(s) to official documentation.
 
-This detailed information ensures you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
+This detailed information ensures that you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
 
 - **Tree-based navigation:** **SIMD.info** uses a clear, hierarchical layout to organize instructions. It categorizes instructions into broad groups like **Arithmetic**, which are further divided into specific subcategories such as **Vector Add** and **Vector Subtract**. This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the exact instructions you need.
 Here is an example of the tree structure:
@@ -46,10 +46,10 @@ Here is an example of the tree structure:
         - **Bit Clear** 
         - **XOR**
 
-- **Advanced search functionality:** With its robust search engine, **SIMD.info** allows you to either search for a specific intrinsic (e.g. `vaddq_f64`) or enter more general terms (e.g. *How to add 2 vectors*), and it will return a list of the corresponding intrinsics. You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
+- **Advanced search functionality:** With its robust search engine, **SIMD.info** allows you to either search for a specific intrinsic (for example `vaddq_f64`) or enter more general terms (for example *How to add 2 vectors*), and it returns a list of the corresponding intrinsics. You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
 
-- **Comparison tools:** This feature lets you directly compare SIMD instructions from different (or the same) platforms side by side, offering a clear view of the similarities and differences. It’s a very helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
+- **Comparison tools:** This feature lets you directly compare SIMD instructions from different (or the same) platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
 
 - **Discussion forum (like StackOverflow):** The integrated discussion forum, powered by **[discuss](https://disqus.com/)** allows users to ask questions, share insights, and troubleshoot problems together. This community-driven space ensures that you’re never stuck on a complex issue without support, fostering collaboration and knowledge-sharing among SIMD developers. Imagine something like **StackOverflow** but specific to SIMD intrinsics.
 
-You can now learn how to use these features in the context of an actual example.
+Now let's look at these features in the context of a real example.

From 99607cc6ad480cdd170945406e9f5b38d5945544 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 25 Nov 2024 17:41:31 +0000
Subject: [PATCH 42/87] Further editorial updates.

---
 .../cross-platform/simd-info-demo/_index.md   |  2 +-
 .../simd-info-demo/intro-to-simdinfo.md       |  2 +-
 .../simd-info-demo/simdinfo-description.md    | 63 +++++++++++--------
 .../simd-info-demo/simdinfo-example1.md       |  8 +--
 4 files changed, 43 insertions(+), 32 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index bc2a2cc83a..7340831211 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -7,7 +7,7 @@ cascade:
 
 minutes_to_complete: 30
 
-who_is_this_for: This is for software developers interested in porting SIMD code across Arm platforms.
+who_is_this_for: This Learning Path is for software developers who are interested in porting SIMD code across Arm platforms.
 
 learning_objectives: 
     - Describe how to use SIMD.info’s tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures and improving code portability.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
index 84466ff1ec..28380fce64 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
@@ -13,4 +13,4 @@ For example, to port software written using Intel intrinsics, such as SSE/AVX/AV
 
 Porting the code between architectures can increase development time and introduce the risk of errors. Currently, developers rely on ISA documentation and must manually search across various vendor platforms such as [Arm Developer](https://developer.arm.com/architectures/instruction-sets/intrinsics/) and [Intel Intrinsics Guide](https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html) to find equivalent instructions.
 
-[SIMD.info](https://simd.info) aims to solve this problem by helping you find equivalent instructions and providing a more streamlined way to adapt your code for different architectures.
+[SIMD.info](https://simd.info) aims to solve this problem by helping developers find equivalent instructions and providing a streamlined way to adapt code for different architectures.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 1b2fb45e12..5184d20851 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -6,50 +6,61 @@ weight: 3
 layout: learningpathall
 ---
 
-### Comprehensive SIMD.info Capabilities
+#### SIMD.info categories of information
 **[SIMD.info](https://simd.info/)** offers a variety of powerful tools to enable developers to work more efficiently with SIMD code across different architectures. 
 
 With a database of over 10,000 intrinsics, it provides detailed information to support effective SIMD development.
 
-For each intrinsic, SIMD.info provides the following information:
+For each intrinsic, SIMD.info provides information in the following categories:
+
+* Purpose: a brief description of what the intrinsic does and the primary use case.
 
-* Purpose: a brief description of what the intrinsic does and its primary use case.
 * Result: an explanation of the output or result of the intrinsic.
+
 * Example: a code snippet demonstrating how to use the intrinsic.
+
 * Prototypes: function prototypes for different programming languages (currently C/C++).
-* Assembly Instruction: the corresponding assembly instruction used by the intrinsic.
-* Notes: any additional notes or caveats about the intrinsic.
-* Architecture: a list of architectures that support the intrinsic.
+
+* Assembly Instruction: the corresponding assembly instruction that the intrinsic uses.
+
+* Notes: any further information about the intrinsic, such as caveats.
+
+* Architecture: a list of architecture that supports the intrinsic.
+
 * Link(s) to official documentation.
 
 This detailed information ensures that you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
 
-- **Tree-based navigation:** **SIMD.info** uses a clear, hierarchical layout to organize instructions. It categorizes instructions into broad groups like **Arithmetic**, which are further divided into specific subcategories such as **Vector Add** and **Vector Subtract**. This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the exact instructions you need.
-Here is an example of the tree structure:
-
-
-    - **Arithmetic** 
-    - **Arithmetic (Complex Numbers)** 
-    - **Boolean Logic & Bit Manipulation** 
-        - **Boolean AND** 
-        - **Boolean AND NOT** 
-            - **Boolean AND NOT 128-bit vector** 
-            - **Boolean AND NOT 16-bit signed integers** 
-            - **Boolean AND NOT 16-bit unsigned integers** 
-            - **Boolean AND NOT 256-bit vector**
-            - **Boolean AND NOT 32-bit floats** 
-            - **Boolean AND NOT 32-bit signed integers** 
+#### Tree-based navigation
+SIMD.info uses a clear, hierarchical layout to present the instructions. It categorizes instructions into broad groups such as **Arithmetic**, which are further divided into specific subcategories such as **Vector Add**, and **Vector Subtract**. 
+
+This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the instructions that you need. Below is an example of the tree structure:
+  
+     - Arithmetic 
+     - Arithmetic (Complex Numbers) 
+       - Boolean Logic & Bit Manipulation 
+       - Boolean AND 
+       - Boolean AND NOT 
+            - Boolean AND NOT 128-bit vector 
+            - Boolean AND NOT 16-bit signed integers 
+            - Boolean AND NOT 16-bit unsigned integers 
+            - Boolean AND NOT 256-bit vector
+            - Boolean AND NOT 32-bit float 
+            - Boolean AND NOT 32-bit signed integers 
                 - AVX512: mm512_andnot_epi32 
                 - NEON: vbic_s32
                 - NEON: vbicq_s32 
                 - VSX: vec_andc 
-        - **Bit Clear** 
-        - **XOR**
+        - Bit Clear 
+        - XOR
 
-- **Advanced search functionality:** With its robust search engine, **SIMD.info** allows you to either search for a specific intrinsic (for example `vaddq_f64`) or enter more general terms (for example *How to add 2 vectors*), and it returns a list of the corresponding intrinsics. You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
+#### Advanced search functionality
+With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors", and it returns a list of the corresponding intrinsics. You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
 
-- **Comparison tools:** This feature lets you directly compare SIMD instructions from different (or the same) platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
+#### Comparison tools
+This feature lets you directly compare SIMD instructions from different, or the same, platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
 
-- **Discussion forum (like StackOverflow):** The integrated discussion forum, powered by **[discuss](https://disqus.com/)** allows users to ask questions, share insights, and troubleshoot problems together. This community-driven space ensures that you’re never stuck on a complex issue without support, fostering collaboration and knowledge-sharing among SIMD developers. Imagine something like **StackOverflow** but specific to SIMD intrinsics.
+#### Discussion forum 
+The integrated discussion forum, powered by **[discuss](https://disqus.com/)**, allows users to ask questions, share insights, and troubleshoot problems together. This community-driven space ensures that you’re never stuck on a complex issue without support. It fosters collaboration and knowledge-sharing among SIMD developers. Imagine something like **[StackOverflow](https://stackoverflow.com/)** but specific to SIMD intrinsics.
 
 Now let's look at these features in the context of a real example.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
index be115692d2..7e08c8ac71 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
@@ -6,9 +6,9 @@ weight: 4
 layout: learningpathall
 ---
 
-Consider the following C example that uses Intel SSE4.2 intrinsics.
+Have a look at the following C example that uses Intel SSE4.2 intrinsics.
 
-On an x86_64 Linux development machine, create a file named `calculation_sse.c` with the contents shown below:
+On an x86_64 Linux development machine, create a file named `calculation_sse.c` using the contents as shown below:
 
 ```C
 #include <xmmintrin.h>
@@ -52,7 +52,7 @@ int main() {
 }
 ```
 
-The program first compares whether elements in one vector are greater than those in another vector, prints the result, and then proceeds to compute the addition of two vectors, multiplies the result with one of the vectors, and finally takes the square root of the multiplication result:
+The program first compares whether elements in one vector are greater than those in another vector, prints the result, and then proceeds to compute the addition of two vectors, multiplies the result with one of the vectors, and finally takes the square root of the multiplication result.
 
 Compile the code on your Linux x86_64 system that supports **SSE4.2**:
 
@@ -78,4 +78,4 @@ Multiplication Result: 2.00 12.00 36.00 80.00
 Square Root Result: 1.41 3.46 6.00 8.94
 ```
 
-It is imperative that you run the code first on an Intel x86_64 reference platform, to make sure you understand how it works and what kind of results are being expected.
+It is imperative that you run the code first on an Intel x86_64 reference platform, to make sure that you understand how it works and what kind of results you can expect.

From ee2641d13b046ceb0d01ee3a23297dc437fdceed Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 25 Nov 2024 18:12:29 +0000
Subject: [PATCH 43/87] Editorial update.

---
 .../simd-info-demo/simdinfo-example1-cont.md              | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
index 6a8e1c4463..59ec4ba710 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
@@ -11,7 +11,7 @@ Now that you have a clear view of the example, you can start the process of port
 
 This is where [SIMD.info](https://simd.info/) comes in.
 
-In SIMD programming, the primary concern is the integrity and accuracy of the calculations. Ensuring that these calculations are done correctly is crucial. Performance almost always comes second.
+In SIMD programming, the primary focus is the integrity and accuracy of the calculations. Ensuring that these calculations are done correctly is crucial. Performance almost always comes second.
 
 For the operations in your **SSE4.2** example, you have the following intrinsics:
 
@@ -20,11 +20,11 @@ For the operations in your **SSE4.2** example, you have the following intrinsics
 - **`_mm_mul_ps`**
 - **`_mm_sqrt_ps`**
 
-To gain a deeper understanding of how these intrinsics work and to get detailed descriptions, you can use the search feature on **SIMD.info**. Simply enter the intrinsic's name into the search bar. You can either select from the suggested results or perform a direct search to find detailed information about each intrinsic.
+To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on **SIMD.info**. Simply enter the intrinsic's name into the search bar. You can either select from the suggested results or perform a direct search to retrieve detailed information about each intrinsic.
 
-1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you get information about it's purpose, result-type, assembly instruction, prototype and an example about it. By clicking the **engine** option **"NEON"** you can find it's [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) will help you find the right one. Based on the prototype provided, you would choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
+1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you get information about its purpose, result-type, assembly instruction, prototype, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) will help you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
 
-2. Moving to the next intrinsic, **`_mm_mul_ps`**, you will use the [Intrinsics Tree](https://simd.info/tag-tree) on **SIMD.info** to find the equivalent. Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. Since you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
+2. Moving to the next intrinsic, **`_mm_mul_ps`**, you can use the [Intrinsics Tree](https://simd.info/tag-tree) on **SIMD.info** to find the equivalent. Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
 
 3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding **NEON** intrinsic is by typing **"Square Root"** into the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section regarding **SSE4.2**, you can clearly see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
 

From 32396304c7595514174db46871bfe7e2ecdbaa8c Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Mon, 25 Nov 2024 16:02:03 -0600
Subject: [PATCH 44/87] First review of OpenCV on Windows Learning Path

---
 .../win-opencv/1-opencv-compilers.md          | 15 ++--
 .../win-opencv/2-1-build-msvc.md              | 72 ++++++++++++-------
 .../win-opencv/2-2-build-clang.md             | 42 ++++++-----
 .../laptops-and-desktops/win-opencv/_index.md |  6 +-
 .../win-opencv/_next-steps.md                 |  8 +--
 .../win-opencv/_review.md                     |  2 +-
 6 files changed, 86 insertions(+), 59 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
index 23001219e6..0395709011 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
@@ -6,7 +6,8 @@ weight: 2
 layout: learningpathall
 ---
 
-## OpenCV
+## What is OpenCV?
+
 OpenCV (Open Source Computer Vision Library) is a popular, open-source library that developers use to build computer vision applications. It provides a set of tools and functions that help you handle tasks related to images and videos without needing to write everything from scratch. 
 
 Here’s what developers should know:
@@ -24,19 +25,19 @@ Here’s what developers should know:
 * __Community and Resources__: Being open-source and widely adopted, there is a large community of developers contributing to and supporting OpenCV. This makes it easier to find tutorials, documentation, and answers to questions.
 
 
-## Compilers for Windows on Arm Development
+## Which compilers are available for Windows on Arm Development?
 
-When building applications for Windows on Arm, both MSVC (Microsoft Visual C++) and Clang are options for developers, each with its own advantages.
+MSVC (Microsoft Visual C++) and Clang are options for developers building Windows on Arm applications.
 
 * __MSVC__: A compiler developed by Microsoft that’s part of the Visual Studio IDE. It’s designed specifically for Windows and integrates well with the Windows development ecosystem.
 
 * __Clang__: An open-source compiler that is part of the LLVM project. It’s known for its modern design and cross-platform capabilities. 
 
-MSVC is the go-to for Windows-focused projects needing seamless integration with Visual Studio. Clang is ideal for cross-platform projects or when using modern C++ features with flexibility. 
+MSVC is ideal for Windows-focused projects needing seamless integration with Visual Studio. Clang is ideal for cross-platform projects or when using modern C++ features.
 
 ## Before you begin
 
-Any Windows on Arm machine which has the required tools installed can be used for this Learning Path. You will learn the build methods using both MSVC and Clang. 
+Any Windows on Arm machine which has the required tools installed can be used for this Learning Path. You will learn how to build OpenCV using both MSVC and Clang. 
 
 Please install the following tools required for both methods.
 
@@ -52,7 +53,7 @@ The instructions were tested with the version 3.28.1
 There is currently no Arm version of Git. Install the 64-bit x86 version.
 {{% /notice %}}
 
-Follow the link to intall the required tools for a method using MSVC.
+Follow the link to install the required tools for a method using MSVC.
 
 * [Visual Studio 2022 or higher](/install-guides/vs-woa). 
 
@@ -62,7 +63,7 @@ The instructions were tested with Visual Studio 2022.
 
 To build using Clang, please install the following.
 
-* [LLVM](install-guides/llvm-woa/)
+* [LLVM](/install-guides/llvm-woa/)
 
 {{% notice Note %}}
 The instructions were tested with the version 18.1.8.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
index 3a3c819e9a..ef2f30856e 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
@@ -6,11 +6,11 @@ weight: 3
 layout: learningpathall
 ---
 
-## Build OpenCV with MSVC
+## How do I build OpenCV with MSVC?
 
-### Clone OpenCV repo
+### Clone the OpenCV repository
 
-Open up a Windows Powershell and checkout the source tree:
+Open up a Windows PowerShell and checkout the source tree:
 
 ```bash
 git clone https://github.com/opencv/opencv
@@ -19,12 +19,14 @@ git checkout tags/4.10.0
 ```
 
 {{% notice Note %}}
-You might be able to use a later version. These steps have been tested with the version 4.10.0.
+You might be able to use a later version, but these steps have been tested with the version 4.10.0.
 {{% /notice %}}
 
 ### Pre-build configuration
 
-Here, you will use CMake from the command line. First, run the following command to run the pre-build configuration. 
+You can use CMake from the command line. 
+
+First, run the following command to run the pre-build configuration. 
 
 ```bash
 mkdir build_msvc
@@ -51,14 +53,14 @@ The given options specify the following:
 - The source code is located one level above the current directory.
 - The build will be performed in the current directory.
 - The Visual Studio 2022 MSVC compiler will be used as the compiler.
-- The built library is generated as a single file that includes all of OpenCV's functionality.
+- The compiled library is generated as a single file that includes all of OpenCV's functionality.
 - Unnecessary options have been disabled, assuming processing on Arm CPUs.
 
 &nbsp;
 
 If the configuration is successful, a message similar to the following should be displayed at the end of the execution:
 
-```
+```output
 -- General configuration for OpenCV 4.10.0 =====================================
 --   Version control:               4.10.0
 --
@@ -96,7 +98,7 @@ If the configuration is successful, a message similar to the following should be
 
 ### Build and install
 
-Now run the following command to build and install:
+Run the following commands to build and install OpenCV:
 
 ```bash
 cmake --build . --config Release
@@ -104,12 +106,12 @@ cmake --build . --target INSTALL --config Release
 ```
 
 {{% notice Note %}}
-The build takes approximately 25 mins on Lenovo X13s
+The build takes approximately 25 mins on a Lenovo X13s
 {{% /notice %}}
 
 &nbsp;
 
-When the build and the install is complete, confirm the shared library have been created:
+When the build and the install steps are complete, confirm the shared library has been created by inspecting the results in the `install/bin` directory:
 
 ```bash { output_lines = "2-11" }
 ls ./install/x64/vc17/bin
@@ -126,6 +128,8 @@ Mode                 LastWriteTime         Length Name
 -a----        08/11/2024     09:03       27179008 opencv_world4100.dll
 ```
 
+Also inspect the `install/lib` directory:
+
 ```bash { output_lines = "2-9" }
 ls ./install/x64/vc17/lib
     Directory: C:\Users\username\work\opencv\build_msvc\install\x64\vc17\lib
@@ -140,7 +144,9 @@ Mode                 LastWriteTime         Length Name
 
 &nbsp;
 
-`opencv_world<version>.lib/dll` will be the library used by your application. Once the library files are correctly generated, run the following command to ensure there are no errors.
+The library used in your application is `opencv_world<version>.lib/dll`. 
+
+Once the library files are correctly generated, run the following command to ensure there are no errors.
 
 ```bash { output_lines = "2" }
 ./install/x64/vc17/bin/opencv_version.exe
@@ -155,23 +161,31 @@ The genereated directory name contains "x64," but there is no need to worry as t
 
 ## Build OpenCV Applications
 
-Once the OpenCV library has been successfully built, the next step is to link it to a simple application and try using it.
+Once the OpenCV library has been successfully created, the next step is to link it to a simple application and try using it.
 
 ### Create a new project in Visual Studio
 
-First, create a new project in Visual Studio. Launch Visual Studio, click `Create a new project` on the initial screen, then select `Empty Project` and click `Next`. On the next screen, set the `Project name` and `Location`. You can choose any name and location, but for this example, we named the project `TestOpenCV`, as shown below. Then click `Create` to generate the new project.
+First, create a new project in Visual Studio. 
+
+Launch Visual Studio, click `Create a new project` on the initial screen, then select `Empty Project` and click `Next`. 
 
-![MSVC project](msvc_project.png "Create a new project")
+On the next screen, set the `Project name` and `Location`. You can choose any name and location, but for this example, name the project `TestOpenCV`, as shown below. 
 
-### Adding a source code
+Click `Create` to generate the new project.
+
+![MSVC project #center](msvc_project.png "Create a new project")
+
+### Add source code
 
  In `Solution Explorer`, right-click the `Source Files` folder, select `Add`, and then `New Item...`. Create a file named `test_opencv.cpp`.
 
-![MSVC add file](msvc_add_file.png "Add a source file")
+![MSVC add file #center](msvc_add_file.png "Add a source file")
 
 &nbsp;
 
-Once the file is created, it will open in the editor. Copy and paste the following program into it and save the file.
+Once the file is created, it will open in the editor. 
+
+Copy and paste the following program into it and save the file.
 
 ```cpp
 #include <opencv2/opencv.hpp>
@@ -189,41 +203,45 @@ int main() {
 }
 ```
 
-This program is a simple example that uses OpenCV's functionality to create a 100x100 black image, draw a blue circle on it, and save it as a file.
+This program is a simple example that uses OpenCV to create a 100x100 black image, draw a blue circle on it, and save it as a file.
 
 ### Configure build settings
 
 Next, select the `Configuration` dropdown menu in the center of the screen and change it from `Debug` to `Release`. At this stage, your screen should look like the example shown below.
 
-![MSVC screenshot](msvc_screen.png "MSVC screenshot")
+![MSVC screenshot #center](msvc_screen.png "MSVC screenshot")
 
 &nbsp;
 
 Now, set up the compile and link settings. Select `Project` from the top menu and click on `TestOpenCV properties`. Edit `Include directories`, `Library directories`, and `Additional dependencies` as shown in the images below, and then click OK.
 
-![MSVC include dir](msvc_include_dir.png "Include directories: Specify the directory containing the OpenCV header files.")
+![MSVC include dir #center](msvc_include_dir.png "Include directories: Specify the directory containing the OpenCV header files.")
 
 &nbsp;
 
-![MSVC link dir](msvc_link_dir.png "Library directories: Specify the directory where the libraries for linking are located.")
+![MSVC link dir #center](msvc_link_dir.png "Library directories: Specify the directory where the libraries for linking are located.")
 
 &nbsp;
 
-![MSVC link lib](msvc_link_lib.png "Additional dependencies: Specify the names of the libraries to link")
+![MSVC link lib #center](msvc_link_lib.png "Additional dependencies: Specify the names of the libraries to link")
 
 &nbsp;
 
 Finally, ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable. Set this in the Windows system settings. After setting the environment variable, restart Visual Studio to apply the changes.
 
-![path setting](set_path.png "Set the DLL dir to the PATH environment variable")
+![path setting #center](set_path.png "Set the DLL dir to the PATH environment variable")
+
+### Build the application
+
+You are now ready to build the application. 
 
-### Run the build
+From the top menu, select `Debug` and click `Start Without Debugging` or press `Ctrl` + `F5`.
 
-Once these steps are complete, you're ready to build. From the top menu, select `Debug` and click `Start Without Debugging` or press `Ctrl` + `F5`.
+If a console window appears showing that the program exited with code 0 and `test_image.png` is generated in the top-level directory of your Visual Studio project, you have succeeded. 
 
-If a console window appears showing that the program exited with code 0 and `test_image.png` is generated in the top-level directory of your Visual Studio project, you have succeeded. When you open the image file, it should look like the example shown below.
+Open the image file, it should look like the example shown below.
 
 ![test_image pic](test_image.png "test_image.png")
 
-Congratulations! You are now ready to create your own OpenCV applications.
+Congratulations! You are now ready to create your own OpenCV applications using MSVC.
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
index d8d8d7ae53..442884131a 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -6,9 +6,9 @@ weight: 4
 layout: learningpathall
 ---
 
-## Build OpenCV with Clang
+## How do I build OpenCV with Clang?
 
-### Clone OpenCV repo
+### Clone the OpenCV repository
 
 Open up a Windows Powershell and checkout the source tree:
 
@@ -19,12 +19,14 @@ git checkout tags/4.10.0
 ```
 
 {{% notice Note %}}
-You might be able to use a later version. These steps have been tested with the version 4.10.0.
+You might be able to use a later version, but these steps have been tested with the version 4.10.0.
 {{% /notice %}}
 
 ### Pre-build configuration
 
-Here, you will use CMake from the command line. First, run the following command to run the pre-build configuration. 
+You can use CMake from the command line. 
+
+First, run the following command to run the pre-build configuration. 
 
 ```bash
 mkdir build_clang
@@ -53,7 +55,7 @@ The given options specify the following:
 - The source code is located one level above the current directory.
 - The build will be performed in the current directory.
 - The Clang compiler will be used as the compiler.
-- The built library is generated as a single file that includes all of OpenCV's functionality.
+- The compiled library is generated as a single file that includes all of OpenCV's functionality.
 - Unnecessary options have been disabled, assuming processing on Arm CPUs.
 
 &nbsp;
@@ -98,7 +100,7 @@ If the configuration is successful, a message similar to the following should be
 
 ### Build and install
 
-Now run the following command to build and install:
+Run the following commands to build and install OpenCV:
 
 ```bash
 ninja
@@ -106,12 +108,12 @@ ninja install
 ```
 
 {{% notice Note %}}
-The build takes approximately 25 mins on Lenovo X13s
+The build takes approximately 25 mins on a Lenovo X13s
 {{% /notice %}}
 
 &nbsp;
 
-When the build and the install is complete, confirm the shared library have been created:
+When the build and the install steps are complete, confirm the shared library has been created by inspecting the results in the `install/bin` directory:
 
 ```bash { output_lines = "2-11" }
 ls ./install/bin
@@ -127,6 +129,8 @@ Mode                 LastWriteTime         Length Name
 -a----        08/11/2024     09:50       20207104 opencv_world4100.dll
 ```
 
+Also inspect the  `install/lib` directory:
+
 ```bash { output_lines = "2-9" }
 ls ./install/lib
     Directory: C:\Users\username\work\opencv\build_clang\install\lib
@@ -141,7 +145,9 @@ Mode                 LastWriteTime         Length Name
 
 &nbsp;
 
-`opencv_world<version>.lib/dll` will be the library used by your application. Once the library files are correctly generated, run the following command to ensure there are no errors.
+The library used in your application is `opencv_world<version>.lib/dll`. 
+
+Once the library files are correctly generated, run the following command to ensure there are no errors.
 
 ```bash { output_lines = "2" }
 ./install/bin/opencv_version.exe
@@ -152,11 +158,11 @@ Mode                 LastWriteTime         Length Name
 
 ## Build OpenCV Applications
 
-Once the OpenCV library has been successfully built, the next step is to link it to a simple application and try using it.
+Once the OpenCV library has been successfully created, you can create a simple application and try using it.
 
 ### Prepare a application program
 
-First, save the following C++ program as `test_opencv.cpp` in an appropriate location. Here, it is assumed that the file is saved under the `build_clang` directory.
+First, use a text editor to save the following C++ program as `test_opencv.cpp` in the `build_clang` directory.
 
 ```cpp
 #include <opencv2/opencv.hpp>
@@ -174,11 +180,12 @@ int main() {
 }
 ```
 
-This program is a simple example that uses OpenCV's functionality to create a 100x100 black image, draw a blue circle on it, and save it as a file.
+This program is a simple example that uses OpenCV to create a 100x100 black image, draw a blue circle on it, and save it as a file.
 
 ### Compile the program
 
-Now, let's compile this code using the command below. 
+Compile the code using the command below: 
+
 ```bash
 clang++ .\test_opencv.cpp -o test_opencv.exe -I.\install\include -L.\install\lib -lopencv_world4100
 ```
@@ -191,7 +198,7 @@ The given options specify the following:
 
 ### Run the program
 
-Now, let's run it. To run this executable, you need to ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable, or place the DLLs in the same location as the executable.
+To run the executable, you need to ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable, or place the DLLs in the same location as the executable.
 
 ```bash
 .\test_opencv.exe
@@ -205,9 +212,10 @@ $env:PATH = "./install/bin;" + $env:PATH
 ```
 {{% /notice %}}
 
-When you execute the command, it should finish quickly, and `test_image.png` should be generated. If the image file looks like the one shown below, it means you've succeeded.
+When you execute the command, it will finish quickly, and `test_image.png` is generated. 
 
-![test_image pic](test_image.png "test_image.png")
+Open the image file, it should look like the example shown below.
 
+![test_image pic](test_image.png "test_image.png")
 
-Congratulations! You are now ready to create your own OpenCV applications.
+Congratulations! You are now ready to create your own OpenCV applications using Clang.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index da15b1c424..561cadd2f9 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -6,11 +6,11 @@ minutes_to_complete: 90
 who_is_this_for: This is an advanced topic for software developers who want to use OpenCV with Windows on Arm devices.
 
 learning_objectives: 
-    - Build OpenCV library with Windows on Arm devices.
-    - Develop applications with OpenCV.
+    - Build the OpenCV library for Windows on Arm devices.
+    - Develop applications using OpenCV.
 
 prerequisites:
-    - A Windows on Arm computer such as Lenovo Thinkpad X13s or a [virtual machine](/learning-paths/cross-platform/woa_azure/).
+    - A Windows on Arm computer such as Lenovo Thinkpad X13s or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
 
 author_primary: Koki Mitsunami
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
index 6870d89e90..e12b500d53 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_next-steps.md
@@ -4,14 +4,14 @@ next_step_guidance: You have successfully built OpenCV on Windows on Arm using M
 recommended_path: /learning-paths/laptops-and-desktops/win_forms/
 
 further_reading:
-    - resource:
-        title: Arm Kleidi Libraries
-        link: https://www.arm.com/products/development-tools/embedded-and-software/kleidi-libraries
-        type: website
     - resource:
         title: OpenCV website
         link: https://opencv.org/
         type: website
+    - resource:
+        title: Arm Kleidi Libraries
+        link: https://www.arm.com/products/development-tools/embedded-and-software/kleidi-libraries
+        type: website
     - resource:
         title: Evolution of SIMD architecture with SVE2 
         link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/sve2
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
index 4903c6186e..6db88cd2cc 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_review.md
@@ -23,7 +23,7 @@ review:
                
     - questions:
         question: >
-            Clang is a compiler that is part of the LLVM project, known for cross-platform support.
+            Clang is a compiler that is part of the LLVM project, known for its cross-platform support.
         answers:
             - "True"
             - "False"

From 5b586ad825ab2e0bca23f074992d0d10a824a0b5 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 26 Nov 2024 02:12:29 +0000
Subject: [PATCH 45/87] Test OpenCV on Windows Learning Path

---
 .../win-opencv/1-opencv-compilers.md             |  2 +-
 .../win-opencv/2-1-build-msvc.md                 |  4 +++-
 .../win-opencv/2-2-build-clang.md                | 16 ++++++++++------
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
index 0395709011..ae3c8e282c 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/1-opencv-compilers.md
@@ -75,6 +75,6 @@ The instructions were tested with the version 18.1.8.
 The instructions were tested with version 1.11.1
 {{% /notice %}}
 
-You use the LLVM Clang and the Ninja generator to build. Set PATH to the paths to your LLVM and Ninja install.
+Make sure LLVM Clang and Ninja are in your search path. If they are not, you can use Windows Control Panel to set the PATH environment variable.
 
 You now have the required development tools installed. Please proceed to the page for the compiler you want to build with.
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
index ef2f30856e..a749ff9aa1 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-1-build-msvc.md
@@ -8,6 +8,8 @@ layout: learningpathall
 
 ## How do I build OpenCV with MSVC?
 
+Follow the steps below to build OpenCV and a test application using the library with the MSVC compiler.
+
 ### Clone the OpenCV repository
 
 Open up a Windows PowerShell and checkout the source tree:
@@ -113,7 +115,7 @@ The build takes approximately 25 mins on a Lenovo X13s
 
 When the build and the install steps are complete, confirm the shared library has been created by inspecting the results in the `install/bin` directory:
 
-```bash { output_lines = "2-11" }
+```bash { output_lines = "2-12" }
 ls ./install/x64/vc17/bin
     Directory: C:\Users\username\work\opencv\build_msvc\install\x64\vc17\bin
 Mode                 LastWriteTime         Length Name
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
index 442884131a..a4d8e7cc70 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -8,6 +8,8 @@ layout: learningpathall
 
 ## How do I build OpenCV with Clang?
 
+Follow the steps below to build OpenCV and a test application using the library with the Clang compiler.
+
 ### Clone the OpenCV repository
 
 Open up a Windows Powershell and checkout the source tree:
@@ -198,11 +200,7 @@ The given options specify the following:
 
 ### Run the program
 
-To run the executable, you need to ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable, or place the DLLs in the same location as the executable.
-
-```bash
-.\test_opencv.exe
-```
+To run the executable, you need to ensure that the directory containing the dynamic libraries (DLLs) is added to the `PATH` environment variable, or place the DLLs in the same location as the executable. 
 
 {{% notice Note %}}
 The command below adds the DLL directory to the beginning of the `PATH` environment variable. Since this is a temporary setting, the `PATH` will revert to its original state when the PowerShell session is closed. To set it permanently, you need to use the Windows system settings or the `[Environment]::SetEnvironmentVariable()` method.
@@ -212,7 +210,13 @@ $env:PATH = "./install/bin;" + $env:PATH
 ```
 {{% /notice %}}
 
-When you execute the command, it will finish quickly, and `test_image.png` is generated. 
+Run the test program:
+
+```bash
+.\test_opencv.exe
+```
+
+When you execute the command, it will finish quickly, and `test_image.png` is generated. If you don't have the DLL directory in your search path, the program appears to run, but no `test_image.png` is generated. 
 
 Open the image file, it should look like the example shown below.
 

From 2d767500403763527bb1d45488e590f95e061b9f Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 26 Nov 2024 10:45:05 +0000
Subject: [PATCH 46/87] Editorial review.

---
 .../cross-platform/simd-info-demo/_index.md   |  4 ++--
 .../cross-platform/simd-info-demo/_review.md  | 24 +++++++++----------
 .../simd-info-demo/conclusion.md              | 14 +++++++----
 .../simd-info-demo/intro-to-simdinfo.md       |  8 ++++---
 .../simd-info-demo/simdinfo-description.md    | 12 ++++++----
 .../simd-info-demo/simdinfo-example1-cont.md  | 10 ++++----
 .../simd-info-demo/simdinfo-example1.md       | 10 ++++++--
 7 files changed, 50 insertions(+), 32 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index 7340831211..43a273c486 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -10,11 +10,11 @@ minutes_to_complete: 30
 who_is_this_for: This Learning Path is for software developers who are interested in porting SIMD code across Arm platforms.
 
 learning_objectives: 
-    - Describe how to use SIMD.info’s tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures and improving code portability.
+    - Describe how to use SIMD.info’s tools and features, such as navigation, search, and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures to improve code portability.
 
 prerequisites:
     - A basic understanding of SIMD.
-    - Access to an Arm platform with a SIMD supported engine, with recent versions of a C compiler such as Clang or GCC installed.
+    - Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.
 
 author_primary: Georgios Mermigkis and Konstantinos Margaritis, VectorCamp
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_review.md b/content/learning-paths/cross-platform/simd-info-demo/_review.md
index cc6a2a64d0..35df3294b6 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_review.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_review.md
@@ -4,34 +4,34 @@ review:
         question: >
             What is SIMD.info?
         answers:
-            - An online resource for SIMD C intrinsics for all major architectures
-            - It's an online forum for SIMD developers
-            - A book about SIMD programming
+            - It's an online resource for SIMD C intrinsics for all major architectures.
+            - It's an online forum for SIMD developers.
+            - It's a book about SIMD programming.
         correct_answer: 1                    
         explanation: >
-            While it allows comments in the SIMD intrinsics, SIMD.info is not really a forum. It is an online **free** resource to assist developers porting C code between popular architectures, for example, from SSE/AVX/AVX512 to Arm ASIMD.
+            While it allows comments in the SIMD intrinsics, SIMD.info is not a forum. It is an online free resource to assist developers porting C code between popular architectures, for example, from SSE/AVX/AVX512 to Arm ASIMD.
 
     - questions:
         question: >
             What architectures are listed in SIMD.info?
         answers:
-            - Intel SSE and Arm ASIMD
-            - Power VSX and Arm ASIMD/SVE
-            - Intel SSE4.2/AVX/AVX2/AVX512, Arm ASIMD, Power VSX
+            - Intel SSE and Arm ASIMD.
+            - Power VSX and Arm ASIMD/SVE.
+            - Intel SSE4.2/AVX/AVX2/AVX512, Arm ASIMD, Power VSX.
         correct_answer: 3
         explanation: >
-            At the time of writing SIMD.info supports Intel SSE4.2/AVX/AVX2/AVX512, Arm ASIMD, Power VSX as SIMD architectures. Work is in progress to include Arm SVE/SVE2, MIPS MSA, RISC-V RVV 1.0, s390 Z and others.
+            SIMD.info supports Intel SSE4.2/AVX/AVX2/AVX512, Arm ASIMD, Power VSX as SIMD architectures. Work is in progress to include Arm SVE/SVE2, MIPS MSA, RISC-V RVV 1.0, s390 Z and others.
 
     - questions:
         question: >
             What are SIMD.info's major features?
         answers:
-            - Hierarchical tree, Search, AI code translation
-            - Search, Hierarchical tree, Code examples
-            - Hierarchical tree, Search, Intrinsics Comparison, Code examples, Equivalents mapping, links to official documentation
+            - Hierarchical tree, search, and AI code translation.
+            - Search, hierarchical tree, and code examples.
+            - Hierarchical tree, search, intrinsics comparison, code examples, equivalents mapping, and links to official documentation.
         correct_answer: 3
         explanation: >
-            SIMD.info provides multiple features, including a hierarchical tree, Search facility, Intrinsics Comparison, Code examples, Equivalents mapping, links to official documentation and others. AI code translation is not a feature of SIMD.info but will be the focus of another project, SIMD.ai.
+            SIMD.info provides multiple features, including a hierarchical tree, search facility, intrinsics comparison, code examples, equivalents mapping, links to official documentation, and others. AI code translation is not a feature of SIMD.info but is the focus of another project, SIMD.ai.
 
 
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
index bf30963645..d7627070b3 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
@@ -8,10 +8,16 @@ layout: learningpathall
 
 ### Conclusion and Additional Resources
 
-Porting SIMD code between architecture can be a daunting process, in many cases requiring many hours of studying multiple ISAs in online resources or ISA manuals of thousands pages. Our primary focus in this work was to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. While reordering data to align with native Arm instructions could offer performance benefits, our scope remained within the constraints of the current data layout and algorithm. For those interested in data layout strategies to further enhance performance on Arm, the [vectorization-friendly data layout learning path](https://learn.arm.com/learning-paths/cross-platform/vectorization-friendly-data-layout/) offers valuable insights.
+Porting SIMD code between architectures can be a daunting process, often requiring many hours of studying multiple ISAs in online resources or ISA manuals that run into thousands of pages. 
 
-Using **[SIMD.info](https://simd.info)** can be be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding **NEON** equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.
+The primary focus of this Learning Path is to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. 
 
-While porting between vectors of different sizes is more complex, work is underway -at the time of writing this guide- to complete integration of **SVE**/**SVE2** Arm extensions and allow matching them with **AVX512** intrinsics, as they are both using predicate masks.
+While reordering data to align with native Arm instructions can offer performance benefits, this is outside the scope of this Learning Path. 
 
-Please check **[SIMD.info](https://simd.info)** regularly for updates on this.
+If you are interested in data layout strategies to further enhance performance on Arm, the [vectorization-friendly data layout learning path](https://learn.arm.com/learning-paths/cross-platform/vectorization-friendly-data-layout/) offers valuable insights.
+
+Using **[SIMD.info](https://simd.info)** can be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding **NEON** equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.
+
+While porting between vectors of different sizes is more complex, work is underway to complete the integration of **SVE**/**SVE2** Arm extensions and allow matching them with **AVX512** intrinsics, as they are both using predicate masks.
+
+You can check **[SIMD.info](https://simd.info)** regularly for updates.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
index 28380fce64..c37e72753d 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
@@ -7,10 +7,12 @@ layout: learningpathall
 ---
 
 ### The Challenge of SIMD Code Portability
-One of the biggest challenges developers face when working with SIMD code is making it portable across different platforms. SIMD instructions are designed to improve performance by executing the same operation on multiple data elements in parallel. Each architecture has its own set of SIMD instructions however, making it difficult to write code that works on all of them without major changes to the code or the algorithm, or both.
+SIMD instructions are designed to improve performance by executing the same operation on multiple data elements in parallel. One of the biggest challenges developers face when working with SIMD code is making it portable across different platforms.  
 
-For example, to port software written using Intel intrinsics, such as SSE/AVX/AVX512, to Arm Neon, you have pay attention to data handling with the different instruction sets.
+Each architecture has its own set of SIMD instructions, which makes it difficult to port code without major changes to either the code itelf, or the algorithm, or both.
+
+For example, to port software written using Intel intrinsics, such as SSE/AVX/AVX512, to Arm Neon, you must address issues with data handling with the different instruction sets.
 
 Porting the code between architectures can increase development time and introduce the risk of errors. Currently, developers rely on ISA documentation and must manually search across various vendor platforms such as [Arm Developer](https://developer.arm.com/architectures/instruction-sets/intrinsics/) and [Intel Intrinsics Guide](https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html) to find equivalent instructions.
 
-[SIMD.info](https://simd.info) aims to solve this problem by helping developers find equivalent instructions and providing a streamlined way to adapt code for different architectures.
+[SIMD.info](https://simd.info) aims to address this challenge by enabling developers to find equivalent instructions and providing a streamlined way to adapt code for different architectures.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 5184d20851..0e2940ee7d 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -9,7 +9,7 @@ layout: learningpathall
 #### SIMD.info categories of information
 **[SIMD.info](https://simd.info/)** offers a variety of powerful tools to enable developers to work more efficiently with SIMD code across different architectures. 
 
-With a database of over 10,000 intrinsics, it provides detailed information to support effective SIMD development.
+With a database of over 10,000 intrinsics, it provides valuable detailed information to support effective SIMD development.
 
 For each intrinsic, SIMD.info provides information in the following categories:
 
@@ -27,12 +27,12 @@ For each intrinsic, SIMD.info provides information in the following categories:
 
 * Architecture: a list of architecture that supports the intrinsic.
 
-* Link(s) to official documentation.
+* Links to official documentation.
 
-This detailed information ensures that you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
+This information ensures that you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
 
 #### Tree-based navigation
-SIMD.info uses a clear, hierarchical layout to present the instructions. It categorizes instructions into broad groups such as **Arithmetic**, which are further divided into specific subcategories such as **Vector Add**, and **Vector Subtract**. 
+SIMD.info uses a clear, hierarchical layout to present the instructions. It categorizes instructions in to high-level groups such as **Arithmetic**, which are then further divided into specific subcategories such as **Vector Add**, and **Vector Subtract**. 
 
 This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the instructions that you need. Below is an example of the tree structure:
   
@@ -55,7 +55,9 @@ This organized structure enables you to browse through SIMD instruction sets acr
         - XOR
 
 #### Advanced search functionality
-With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors", and it returns a list of the corresponding intrinsics. You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
+With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors", and it returns a list of the corresponding intrinsics. 
+
+You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, or **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
 
 #### Comparison tools
 This feature lets you directly compare SIMD instructions from different, or the same, platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
index 59ec4ba710..a9449b1cbf 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
@@ -11,7 +11,7 @@ Now that you have a clear view of the example, you can start the process of port
 
 This is where [SIMD.info](https://simd.info/) comes in.
 
-In SIMD programming, the primary focus is the integrity and accuracy of the calculations. Ensuring that these calculations are done correctly is crucial. Performance almost always comes second.
+In SIMD programming, the primary focus is the integrity and accuracy of the calculations. Ensuring that these calculations are done correctly is crucial. Performance is almost always a secondary concern.
 
 For the operations in your **SSE4.2** example, you have the following intrinsics:
 
@@ -20,11 +20,13 @@ For the operations in your **SSE4.2** example, you have the following intrinsics
 - **`_mm_mul_ps`**
 - **`_mm_sqrt_ps`**
 
-To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on **SIMD.info**. Simply enter the intrinsic's name into the search bar. You can either select from the suggested results or perform a direct search to retrieve detailed information about each intrinsic.
+To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on SIMD.info. Simply enter the intrinsic's name into the search bar. You can either select from the suggested results or perform a direct search to retrieve detailed information about each intrinsic.
 
-1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you get information about its purpose, result-type, assembly instruction, prototype, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) will help you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
+1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you will retrieve information about its purpose, the result type, assembly instruction, prototype, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) helps you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
 
-2. Moving to the next intrinsic, **`_mm_mul_ps`**, you can use the [Intrinsics Tree](https://simd.info/tag-tree) on **SIMD.info** to find the equivalent. Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
+2. Moving to the next intrinsic, **`_mm_mul_ps`**, you can use the [Intrinsics Tree](https://simd.info/tag-tree) on SIMD.info to find the equivalent. 
+
+Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
 
 3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding **NEON** intrinsic is by typing **"Square Root"** into the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section regarding **SSE4.2**, you can clearly see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
index 7e08c8ac71..542872e3ec 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
@@ -8,7 +8,7 @@ layout: learningpathall
 
 Have a look at the following C example that uses Intel SSE4.2 intrinsics.
 
-On an x86_64 Linux development machine, create a file named `calculation_sse.c` using the contents as shown below:
+On an x86_64 Linux development machine, create a file named `calculation_sse.c`, populating it with the contents as shown below:
 
 ```C
 #include <xmmintrin.h>
@@ -52,7 +52,13 @@ int main() {
 }
 ```
 
-The program first compares whether elements in one vector are greater than those in another vector, prints the result, and then proceeds to compute the addition of two vectors, multiplies the result with one of the vectors, and finally takes the square root of the multiplication result.
+The program does the following:
+
+* Compares whether elements in one vector are greater than those in another vector.
+* Prints the result.
+* Computes the addition of two vectors.
+* Multiplies the result with one of the vectors.
+* Takes the square root of the multiplication result.
 
 Compile the code on your Linux x86_64 system that supports **SSE4.2**:
 

From 11c768ba6e36b29ebc7122f1cdd81103a9bd8e88 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 26 Nov 2024 11:43:07 +0000
Subject: [PATCH 47/87] Editorial.

---
 .../simd-info-demo/_next-steps.md             |  2 +-
 .../simd-info-demo/conclusion.md              | 14 ++++++-------
 .../simd-info-demo/intro-to-simdinfo.md       |  2 +-
 .../simd-info-demo/simdinfo-description.md    | 20 +++++++++----------
 .../simd-info-demo/simdinfo-example1-cont.md  |  6 +++---
 .../simdinfo-example1-porting.md              | 18 +++++++++--------
 .../simd-info-demo/simdinfo-example1.md       |  2 +-
 .../simd-info-demo/simdinfo-example2.md       | 12 ++++++-----
 8 files changed, 39 insertions(+), 37 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/_next-steps.md b/content/learning-paths/cross-platform/simd-info-demo/_next-steps.md
index 320c29c6e1..2c6760d15e 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_next-steps.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_next-steps.md
@@ -1,7 +1,7 @@
 ---
 next_step_guidance: You should explore **SIMD.info** more and find out porting opportunities between different SIMD engines.
 
-recommended_path: /learning-paths/cross-platform/
+recommended_path: /learning-paths/cross-platform/vectorization-friendly-data-layout/
 
 further_reading:
     - resource:
diff --git a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
index d7627070b3..d16f95a0aa 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
@@ -6,18 +6,16 @@ weight: 8
 layout: learningpathall
 ---
 
-### Conclusion and Additional Resources
+### Conclusion and further reading
 
 Porting SIMD code between architectures can be a daunting process, often requiring many hours of studying multiple ISAs in online resources or ISA manuals that run into thousands of pages. 
 
-The primary focus of this Learning Path is to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. 
+The primary focus of this Learning Path is to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. While reordering data to align with native Arm instructions can offer performance benefits, this is outside the scope of this Learning Path. 
 
-While reordering data to align with native Arm instructions can offer performance benefits, this is outside the scope of this Learning Path. 
+If you are interested in data layout strategies to further enhance performance on Arm, see the Learning Path *Optimize SIMD code with vectorization-friendly data layout* linked to in the **Next Steps** section at the end. This provides valuable insights.
 
-If you are interested in data layout strategies to further enhance performance on Arm, the [vectorization-friendly data layout learning path](https://learn.arm.com/learning-paths/cross-platform/vectorization-friendly-data-layout/) offers valuable insights.
+Using SIMD.info can be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding NEON equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.
 
-Using **[SIMD.info](https://simd.info)** can be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding **NEON** equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.
+While porting between vectors of different sizes is more complex, work is underway to complete the integration of SVE and SVE2 Arm extensions and allow matching them with AVX512 intrinsics, as they both use predicate masks.
 
-While porting between vectors of different sizes is more complex, work is underway to complete the integration of **SVE**/**SVE2** Arm extensions and allow matching them with **AVX512** intrinsics, as they are both using predicate masks.
-
-You can check **[SIMD.info](https://simd.info)** regularly for updates.
+You can check **[SIMD.info](https://simd.info)** for updates.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
index c37e72753d..65ca70f73f 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/intro-to-simdinfo.md
@@ -9,7 +9,7 @@ layout: learningpathall
 ### The Challenge of SIMD Code Portability
 SIMD instructions are designed to improve performance by executing the same operation on multiple data elements in parallel. One of the biggest challenges developers face when working with SIMD code is making it portable across different platforms.  
 
-Each architecture has its own set of SIMD instructions, which makes it difficult to port code without major changes to either the code itelf, or the algorithm, or both.
+Each architecture has its own set of SIMD instructions, which makes it difficult to port code without major changes to either the code itself, or the algorithm, or both.
 
 For example, to port software written using Intel intrinsics, such as SSE/AVX/AVX512, to Arm Neon, you must address issues with data handling with the different instruction sets.
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 0e2940ee7d..1ebe0c9572 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -13,26 +13,26 @@ With a database of over 10,000 intrinsics, it provides valuable detailed informa
 
 For each intrinsic, SIMD.info provides information in the following categories:
 
-* Purpose: a brief description of what the intrinsic does and the primary use case.
+* **Purpose**: a brief description of what the intrinsic does and the primary use case.
 
-* Result: an explanation of the output or result of the intrinsic.
+* **Result**: an explanation of the output or result of the intrinsic.
 
-* Example: a code snippet demonstrating how to use the intrinsic.
+* **Example**: a code snippet demonstrating how to use the intrinsic.
 
-* Prototypes: function prototypes for different programming languages (currently C/C++).
+* **Prototypes**: function prototypes for different programming languages (currently C/C++).
 
-* Assembly Instruction: the corresponding assembly instruction that the intrinsic uses.
+* **Assembly Instruction**: the corresponding assembly instruction that the intrinsic uses.
 
-* Notes: any further information about the intrinsic, such as caveats.
+* **Notes**: any further information about the intrinsic, such as caveats.
 
-* Architecture: a list of architecture that supports the intrinsic.
+* **Architecture**: a list of architecture that supports the intrinsic.
 
-* Links to official documentation.
+* **Links to official documentation**.
 
 This information ensures that you have all the necessary resources to effectively use and port SIMD instructions across different platforms. Each feature is designed to simplify navigation, improve the search for equivalent instructions, and foster a collaborative environment for knowledge-sharing.
 
 #### Tree-based navigation
-SIMD.info uses a clear, hierarchical layout to present the instructions. It categorizes instructions in to high-level groups such as **Arithmetic**, which are then further divided into specific subcategories such as **Vector Add**, and **Vector Subtract**. 
+SIMD.info uses a clear, hierarchical layout to present the instructions. It categorizes instructions into high-level groups such as **Arithmetic**, which are then further divided into specific subcategories such as **Vector Add**, and **Vector Subtract**. 
 
 This organized structure enables you to browse through SIMD instruction sets across various platforms, allowing you to efficiently find and access the instructions that you need. Below is an example of the tree structure:
   
@@ -57,7 +57,7 @@ This organized structure enables you to browse through SIMD instruction sets acr
 #### Advanced search functionality
 With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors", and it returns a list of the corresponding intrinsics. 
 
-You can also filter results based on the specific engine you're working with, such as **NEON**, **SSE4.2**, **AVX**, **AVX512**, or **VSX**. This functionality streamlines the process of finding the right commands tailored to your needs.
+You can also filter results based on the specific engine you're working with, such as NEON, SSE4.2, AVX, AVX512, or VSX. This functionality streamlines the process of finding the right commands tailored to your needs.
 
 #### Comparison tools
 This feature lets you directly compare SIMD instructions from different, or the same, platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
index a9449b1cbf..c903ef320d 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
@@ -20,7 +20,7 @@ For the operations in your **SSE4.2** example, you have the following intrinsics
 - **`_mm_mul_ps`**
 - **`_mm_sqrt_ps`**
 
-To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on SIMD.info. Simply enter the intrinsic's name into the search bar. You can either select from the suggested results or perform a direct search to retrieve detailed information about each intrinsic.
+To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on SIMD.info. Simply enter the name of the intrinsic into the search bar. You can either select from the suggested results or perform a direct search to retrieve information about each intrinsic.
 
 1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you will retrieve information about its purpose, the result type, assembly instruction, prototype, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) helps you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
 
@@ -28,8 +28,8 @@ To gain a deeper understanding of how these intrinsics work and to surface detai
 
 Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
 
-3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding **NEON** intrinsic is by typing **"Square Root"** into the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section regarding **SSE4.2**, you can clearly see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
+3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding NEON intrinsic is by typing **"Square Root"** into the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section regarding **SSE4.2**, you can clearly see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
 
 4. For the last intrinsic, **`_mm_cmpgt_ps`**, follow a similar approach as before. Inside the intrinsics tree, start by expanding the **Comparison** folder. Navigate to the subfolder **Vector Compare Greater Than**, and since you are working with 32-bit floats, proceed to **Vector Compare Greater Than 32-bit floats**. The recommended choice is again the 128-bit variant [**`vcgtq_f32`**](https://simd.info/c_intrinsic/vcgtq_f32/).
 
-Now that you have found the **NEON** equivalents for each **SSE4.2** intrinsic, you're ready to begin porting the code. Understanding these equivalents is key to ensuring that the code produces the correct results in the calculations as you switch between SIMD engines.
+Now that you have found the NEON equivalents for each SSE4.2 intrinsic, you're ready to begin porting the code. Understanding these equivalents is key to ensuring that the code produces the correct results in the calculations as you switch between SIMD engines.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
index f0a2d3f5bb..f6b15af718 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
@@ -8,13 +8,15 @@ layout: learningpathall
 
 ### Step-by-Step Porting
 
-1. Change the loading process to follow **NEON**'s method for initializing vectors. The **SSE4.2** intrinsic **`_mm_set_ps`** is in reality a macro, in **NEON** you can do the same thing with curly braces **`{}`** inititialization.
-2. Next, you will replace the **SSE4.2** intrinsics with the **NEON** equivalents we identified earlier. The key is to ensure that the operations perform the same tasks, such as comparison, addition, multiplication, and square root calculations.
-3. Finally, modify the storing process to match **NEON**’s way of moving data from vectors to memory. In **NEON**, you use functions like [**`vst1q_f32`**](https://simd.info/c_intrinsic/vst1q_f32/) for storing 128-bit floating-point vectors and [**`vst1q_u32`**](https://simd.info/c_intrinsic/vst1q_u32/) for storing 128-bit integer vectors.
+Follow this step-by-step process to porting:
 
-After identifying the **NEON** intrinsics you will need in the ported program, it's time to actually write the code.
+1. Change the loading process to follow NEON's method for initializing vectors. The SSE4.2 intrinsic **`_mm_set_ps`** is in reality a macro, in NEON you can do the same thing with curly braces **`{}`** inititialization.
+2. Next, replace the SSE4.2 intrinsics with the NEON equivalents that you identified earlier. The key is to ensure that the operations perform the same tasks, such as comparison, addition, multiplication, and square root calculations.
+3. Finally, modify the storing process to match NEON’s way of moving data from vectors to memory. In NEON, you use functions like [**`vst1q_f32`**](https://simd.info/c_intrinsic/vst1q_f32/) for storing 128-bit floating-point vectors and [**`vst1q_u32`**](https://simd.info/c_intrinsic/vst1q_u32/) for storing 128-bit integer vectors.
 
-This time on your Arm Linux machine, create a new file for the ported NEON code named `calculation_neon.c` with the contents shown below:
+After identifying the NEON intrinsics that you require in the ported program, it's now time to write the code.
+
+This time on your Arm Linux machine, create a new file for the ported NEON code named `calculation_neon.c`, populating with the contents as shown below:
 
 ```C
 #include <arm_neon.h>
@@ -66,7 +68,7 @@ int main() {
 
 ### Verifying the Ported Code
 
-It's time to verify that the functionality remains the same, which means you get the same results and similar performance.
+It's time to verify that the functionality remains the same, which means that you achieve the same results and similar performance.
 
 Compile the above code as follows on your Arm Linux machine:
 
@@ -92,8 +94,8 @@ Multiplication Result: 2.00 12.00 36.00 80.00
 Square Root Result: 1.41 3.46 6.00 8.94
 ```
 
-You can see that the results are the same as in the **SSE4.2** example.
+You can see that the results are the same as in the SSE4.2 example.
 
 {{% notice Note %}} 
-You initialized the vectors in reverse order compared to the **SSE4.2** version because the array initialization and vld1q_f32 function load vectors from LSB to MSB, whereas **`_mm_set_ps`** loads elements MSB to LSB.
+You initialized the vectors in reverse order compared to the SSE4.2 version because the array initialization and vld1q_f32 function load vectors from LSB to MSB, whereas **`_mm_set_ps`** loads elements MSB to LSB.
 {{% /notice %}}
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
index 542872e3ec..2e3eb72d9d 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1.md
@@ -60,7 +60,7 @@ The program does the following:
 * Multiplies the result with one of the vectors.
 * Takes the square root of the multiplication result.
 
-Compile the code on your Linux x86_64 system that supports **SSE4.2**:
+Compile the code on your Linux x86_64 system that supports SSE4.2:
 
 ```bash
 gcc -O3 calculation_sse.c -o calculation_sse -msse4.2
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
index 32793cf3c0..5f37dc9002 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
@@ -8,11 +8,13 @@ layout: learningpathall
 
 ### Handling intrinsics without direct equivalents
 
-During the porting process, you will observe that certain instructions translate seamlessly. However, there are cases where direct equivalents for some intrinsics may not be readily available across architectures. For example, the [**`_mm_madd_epi16`**](https://simd.info/c_intrinsic/_mm_madd_epi16/) intrinsic from **SSE2**, which performs multiplication of 16-bit signed integer elements in a vector and then does a pairwise addition of adjacent elements increasing the element width, does not have a direct counterpart in **NEON**. However it can be emulated using another intrinsic. Similarly its 256 and 512-bit counterparts, [**`_mm256_madd_epi16`**](https://simd.info/c_intrinsic/_mm256_madd_epi16/) and [**`_mm512_madd_epi16`**](https://simd.info/c_intrinsic/_mm512_madd_epi16/) can be emulated by a sequence of instructions, but here you will see the 128-bit variant.
+During the porting process, you can see that certain instructions translate seamlessly. However, there are cases where direct equivalents for some intrinsics might not be readily available across architectures. 
 
-You may already know the equivalent operations for this particular intrinsic, but let's assume you don't. In this usecase, reading the **`_mm_madd_epi16`** on the **SIMD.info** might indicate that a key characteristic of the instruction involved is the *widening* of the result elements, from 16-bit to 32-bit signed integers. Unfortunately, that is not the case, as this particular instruction does not actually increase the size of the element holding the result values. You will see how that effects the result in the example.
+For example, the [**`_mm_madd_epi16`**](https://simd.info/c_intrinsic/_mm_madd_epi16/) intrinsic from SSE2, which performs multiplication of 16-bit signed integer elements in a vector and then does a pairwise addition of adjacent elements increasing the element width, does not have a direct counterpart in NEON. However it can be emulated using another intrinsic. Similarly its 256 and 512-bit counterparts, [**`_mm256_madd_epi16`**](https://simd.info/c_intrinsic/_mm256_madd_epi16/) and [**`_mm512_madd_epi16`**](https://simd.info/c_intrinsic/_mm512_madd_epi16/), can be emulated by a sequence of instructions, but here you will see the 128-bit variant.
 
-Consider the following code for **SSE2**. Create a new file on your x86_64 Linux machine named `_mm_madd_epi16_test.c` with the contents shown below:
+You might already know the equivalent operations for this particular intrinsic, but let's assume that you don't. In this particular use case, reading **`_mm_madd_epi16`** on **SIMD.info** might indicate that a key characteristic of the instruction involved is the widening of the result elements, from 16-bit to 32-bit signed integers. Unfortunately, this is not the case. This particular instruction does not increase the size of the element holding the result values. You will see how this affects the result in the example.
+
+Consider the following code for SSE2. Create a new file on your x86_64 Linux machine named `_mm_madd_epi16_test.c`, and populate with the contents as shown below:
 
 ```C
 #include <stdint.h>
@@ -44,7 +46,7 @@ int main() {
 }
 ```
 
-Compile the code as follows on the x86_64 system (no extra flags required as **SSE2** is assumed by default on all 64-bit x86 systems):
+Compile the code as follows on the x86_64 system. No extra flags are required as **SSE2** is assumed by default on all 64-bit x86 systems:
 ```bash
 gcc -O3 _mm_madd_epi16_test.c -o  _mm_madd_epi16_test
 ```
@@ -128,5 +130,5 @@ vpaddq_s16(a, b)              : a4d8 56b8 2198  578    0    0    0    0
 final                         : a4d8    0 56b8    0 2198    0  578    0
 ```
 
-As you can see the results of both executions on different architectures match. You were able to use **SIMD.info** to help with the translation of complex intrinsics between different SIMD architectures.
+As you can see the results of both executions on different architectures match. You used SIMD.info to help with the translation of complex intrinsics between different SIMD architectures.
 

From 172dc75d2e0514b43942e5ed21f88e8d00d8f7c5 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 26 Nov 2024 11:48:18 +0000
Subject: [PATCH 48/87] Editorial.

---
 .../cross-platform/simd-info-demo/simdinfo-example2.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
index 5f37dc9002..feb24da071 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
@@ -10,7 +10,7 @@ layout: learningpathall
 
 During the porting process, you can see that certain instructions translate seamlessly. However, there are cases where direct equivalents for some intrinsics might not be readily available across architectures. 
 
-For example, the [**`_mm_madd_epi16`**](https://simd.info/c_intrinsic/_mm_madd_epi16/) intrinsic from SSE2, which performs multiplication of 16-bit signed integer elements in a vector and then does a pairwise addition of adjacent elements increasing the element width, does not have a direct counterpart in NEON. However it can be emulated using another intrinsic. Similarly its 256 and 512-bit counterparts, [**`_mm256_madd_epi16`**](https://simd.info/c_intrinsic/_mm256_madd_epi16/) and [**`_mm512_madd_epi16`**](https://simd.info/c_intrinsic/_mm512_madd_epi16/), can be emulated by a sequence of instructions, but here you will see the 128-bit variant.
+For example, the [**`_mm_madd_epi16`**](https://simd.info/c_intrinsic/_mm_madd_epi16/) intrinsic from SSE2, which performs multiplication of 16-bit signed integer elements in a vector and then does a pairwise addition of adjacent elements increasing the element width, does not have a direct counterpart in NEON. However, it can be emulated using another intrinsic. Similarly its 256 and 512-bit counterparts, [**`_mm256_madd_epi16`**](https://simd.info/c_intrinsic/_mm256_madd_epi16/) and [**`_mm512_madd_epi16`**](https://simd.info/c_intrinsic/_mm512_madd_epi16/), can be emulated by a sequence of instructions, but here you will see the 128-bit variant.
 
 You might already know the equivalent operations for this particular intrinsic, but let's assume that you don't. In this particular use case, reading **`_mm_madd_epi16`** on **SIMD.info** might indicate that a key characteristic of the instruction involved is the widening of the result elements, from 16-bit to 32-bit signed integers. Unfortunately, this is not the case. This particular instruction does not increase the size of the element holding the result values. You will see how this affects the result in the example.
 
@@ -63,11 +63,11 @@ b                             :   a0   8c   78   64   50   3c   28   14
 _mm_madd_epi16(a, b)          : a4d8    0 56b8    0 2198    0  578    0
 ```
 
-You will note that the result of the first element is a negative number, even though we added 2 positive results (`130*140` and `150*160`). That is because the result of the addition has to occupy a 16-bit signed integer element and when the first is larger we have the effect of an negative overflow. The result is the same in binary arithmetic, but when interpreted into a signed integer, it turns the number into a negative.
+You will note that the result of the first element is a negative number, even though you added 2 positive results (`130*140` and `150*160`). This is because the result of the addition has to occupy a 16-bit signed integer element, and when the first is larger we have the effect of an negative overflow. The result is the same in binary arithmetic, but when interpreted into a signed integer, it turns the number into a negative.
 
-The rest of the values are as expected. Notice how each pair has a zero element next to it. The results are correct, but they are not in the correct order. In this example, you used **`vmovl`** to zero-extend values, which achieves the correct order with zero elements in place. While both **`vmovl`** and **`zip`** could be used for this purpose, **`vmovl`** was chosen in this implementation. For more details, see the Arm Software Optimization Guides, such as the [Neoverse V2 guide](https://developer.arm.com/documentation/109898/latest/).
+The rest of the values are as expected. Notice how each pair has a zero element next to it. The results are correct, but they are not in the correct order. In this example, you used **`vmovl`** to zero-extend values, which achieves the correct order with zero elements in place. While both **`vmovl`** and **`zip`** can be used for this purpose, **`vmovl`** was chosen in this implementation. For more details, see the Arm Software Optimization Guides, such as the [Neoverse V2 guide](https://developer.arm.com/documentation/109898/latest/).
 
-Now switch your Linux Arm machine and create a file called `_mm_madd_epi16_neon.c` with the contents below:
+Now switch to your Linux Arm machine and create a file called `_mm_madd_epi16_neon.c`, populating it with the contents below:
 ```C
 #include <arm_neon.h>
 #include <stdint.h>
@@ -130,5 +130,5 @@ vpaddq_s16(a, b)              : a4d8 56b8 2198  578    0    0    0    0
 final                         : a4d8    0 56b8    0 2198    0  578    0
 ```
 
-As you can see the results of both executions on different architectures match. You used SIMD.info to help with the translation of complex intrinsics between different SIMD architectures.
+As you can see, the results of both executions on different architectures match. You used SIMD.info to help with the translation of complex intrinsics between different SIMD architectures.
 

From 8121d5579993f46bfd8d8176a9cebe24754f2db4 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 26 Nov 2024 15:33:50 +0000
Subject: [PATCH 49/87] Editorial

---
 .../simd-info-demo/simdinfo-description.md         |  4 ++--
 .../simd-info-demo/simdinfo-example1-cont.md       | 14 +++++++-------
 .../simd-info-demo/simdinfo-example1-porting.md    |  2 +-
 .../simd-info-demo/simdinfo-example2.md            |  2 +-
 4 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
index 1ebe0c9572..9997a8fbf5 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-description.md
@@ -55,7 +55,7 @@ This organized structure enables you to browse through SIMD instruction sets acr
         - XOR
 
 #### Advanced search functionality
-With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors", and it returns a list of the corresponding intrinsics. 
+With its robust search engine, SIMD.info allows you to either search for a specific intrinsic, for example `vaddq_f64`, or enter more general terms, for example "How to add 2 vectors," and it returns a list of the corresponding intrinsics. 
 
 You can also filter results based on the specific engine you're working with, such as NEON, SSE4.2, AVX, AVX512, or VSX. This functionality streamlines the process of finding the right commands tailored to your needs.
 
@@ -63,6 +63,6 @@ You can also filter results based on the specific engine you're working with, su
 This feature lets you directly compare SIMD instructions from different, or the same, platforms side by side, offering a clear view of the similarities and differences. It’s a helpful tool for porting code across architectures, as it ensures accuracy and efficiency.
 
 #### Discussion forum 
-The integrated discussion forum, powered by **[discuss](https://disqus.com/)**, allows users to ask questions, share insights, and troubleshoot problems together. This community-driven space ensures that you’re never stuck on a complex issue without support. It fosters collaboration and knowledge-sharing among SIMD developers. Imagine something like **[StackOverflow](https://stackoverflow.com/)** but specific to SIMD intrinsics.
+The integrated discussion forum, powered by **[Disqus](https://disqus.com/)**, allows users to ask questions, share insights, and troubleshoot problems together. This community-driven space ensures that you’re never stuck on a complex issue without support. It fosters collaboration and knowledge-sharing among SIMD developers. Imagine something like **[StackOverflow](https://stackoverflow.com/)** but specific to SIMD intrinsics.
 
 Now let's look at these features in the context of a real example.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
index c903ef320d..495bc4cb5e 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-cont.md
@@ -7,29 +7,29 @@ layout: learningpathall
 ---
 
 ### Using SIMD.info to find NEON Equivalents
-Now that you have a clear view of the example, you can start the process of porting the code to Arm **Neon/ASIMD**.
+Now that you have a clear view of the example, you can start the process of porting the code to Arm Neon/ASIMD.
 
 This is where [SIMD.info](https://simd.info/) comes in.
 
 In SIMD programming, the primary focus is the integrity and accuracy of the calculations. Ensuring that these calculations are done correctly is crucial. Performance is almost always a secondary concern.
 
-For the operations in your **SSE4.2** example, you have the following intrinsics:
+For the operations in your SSE4.2 example, you have the following intrinsics:
 
 - **`_mm_cmpgt_ps`**
 - **`_mm_add_ps`**
 - **`_mm_mul_ps`**
 - **`_mm_sqrt_ps`**
 
-To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on SIMD.info. Simply enter the name of the intrinsic into the search bar. You can either select from the suggested results or perform a direct search to retrieve information about each intrinsic.
+To gain a deeper understanding of how these intrinsics work and to surface detailed descriptions, you can use the search feature on SIMD.info. Simply enter the name of the intrinsic in the search bar. You can either select from the suggested results or perform a direct search to retrieve information about each intrinsic.
 
-1. By searching [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you will retrieve information about its purpose, the result type, assembly instruction, prototype, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) helps you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) because it works with 128-bit vectors which is the same as **SSE4.2**.
+1. By searching for [**`_mm_add_ps`**](https://simd.info/c_intrinsic/_mm_add_ps/) you will retrieve information about its purpose, the result type, assembly instructions, prototypes, and an example demonstration. By clicking the **engine** option **"NEON"** you can find its [equivalents](https://simd.info/eq/_mm_add_ps/NEON/) for this engine. The equivalents are: **`vaddq_f32`**, **`vadd_f32`**. [Intrinsics comparison](https://simd.info/c-intrinsics-compare?compare=vaddq_f32:vadd_f32) helps you find the right one. Based on the prototype provided, you can choose [**`vaddq_f32`**](https://simd.info/c_intrinsic/vaddq_f32/) as it works with 128-bit vectors which is the same as **SSE4.2**.
 
 2. Moving to the next intrinsic, **`_mm_mul_ps`**, you can use the [Intrinsics Tree](https://simd.info/tag-tree) on SIMD.info to find the equivalent. 
 
-Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before—it operates on 128-bit vectors.
+Start by expanding the **Arithmetic** branch and then navigate to the branch **Vector Multiply**. As you are working with 32-bit floats, open the **Vector Multiply 32-bit floats** branch, where you will find several options. The recommended choice is [**`vmulq_f32`**](https://simd.info/c_intrinsic/vmulq_f32/), following the same reasoning as before; it operates on 128-bit vectors.
 
-3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding NEON intrinsic is by typing **"Square Root"** into the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section regarding **SSE4.2**, you can clearly see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
+3. For the third intrinsic, **`_mm_sqrt_ps`**, the easiest way to find the corresponding NEON intrinsic is by typing **"Square Root"** in the search bar on SIMD.info. From the [search results](https://simd.info/search?search=Square+Root&simd_engines=1&simd_engines=2&simd_engines=3&simd_engines=4&simd_engines=5), look for the float-specific version and select [**`vsqrtq_f32`**](https://simd.info/c_intrinsic/vsqrtq_f32/), which, like the others, works with 128-bit vectors. In the equivalents section about **SSE4.2**, you can see that **`_mm_sqrt_ps`** has its place as a direct match for this operation.
 
-4. For the last intrinsic, **`_mm_cmpgt_ps`**, follow a similar approach as before. Inside the intrinsics tree, start by expanding the **Comparison** folder. Navigate to the subfolder **Vector Compare Greater Than**, and since you are working with 32-bit floats, proceed to **Vector Compare Greater Than 32-bit floats**. The recommended choice is again the 128-bit variant [**`vcgtq_f32`**](https://simd.info/c_intrinsic/vcgtq_f32/).
+4. For the last intrinsic, **`_mm_cmpgt_ps`**, follow a similar approach as before. Inside the intrinsics tree, start by expanding the **Comparison** folder. Navigate to the subfolder **Vector Compare Greater Than**, and as you are working with 32-bit floats, proceed to **Vector Compare Greater Than 32-bit floats**. The recommended choice is again the 128-bit variant[**`vcgtq_f32`**](https://simd.info/c_intrinsic/vcgtq_f32/).
 
 Now that you have found the NEON equivalents for each SSE4.2 intrinsic, you're ready to begin porting the code. Understanding these equivalents is key to ensuring that the code produces the correct results in the calculations as you switch between SIMD engines.
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
index f6b15af718..c38523ce30 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example1-porting.md
@@ -10,7 +10,7 @@ layout: learningpathall
 
 Follow this step-by-step process to porting:
 
-1. Change the loading process to follow NEON's method for initializing vectors. The SSE4.2 intrinsic **`_mm_set_ps`** is in reality a macro, in NEON you can do the same thing with curly braces **`{}`** inititialization.
+1. Change the loading process to follow NEON's method for initializing vectors. The SSE4.2 intrinsic **`_mm_set_ps`** is in reality a macro, in NEON you can do the same thing with curly braces **`{}`** initialization.
 2. Next, replace the SSE4.2 intrinsics with the NEON equivalents that you identified earlier. The key is to ensure that the operations perform the same tasks, such as comparison, addition, multiplication, and square root calculations.
 3. Finally, modify the storing process to match NEON’s way of moving data from vectors to memory. In NEON, you use functions like [**`vst1q_f32`**](https://simd.info/c_intrinsic/vst1q_f32/) for storing 128-bit floating-point vectors and [**`vst1q_u32`**](https://simd.info/c_intrinsic/vst1q_u32/) for storing 128-bit integer vectors.
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
index feb24da071..75349e6289 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/simdinfo-example2.md
@@ -63,7 +63,7 @@ b                             :   a0   8c   78   64   50   3c   28   14
 _mm_madd_epi16(a, b)          : a4d8    0 56b8    0 2198    0  578    0
 ```
 
-You will note that the result of the first element is a negative number, even though you added 2 positive results (`130*140` and `150*160`). This is because the result of the addition has to occupy a 16-bit signed integer element, and when the first is larger we have the effect of an negative overflow. The result is the same in binary arithmetic, but when interpreted into a signed integer, it turns the number into a negative.
+You will note that the result of the first element is a negative number, even though you added 2 positive results (`130*140` and `150*160`). This is because the result of the addition has to occupy a 16-bit signed integer element, and when the first is larger we have the effect of a negative overflow. The result is the same in binary arithmetic, but when interpreted into a signed integer, it turns the number into a negative.
 
 The rest of the values are as expected. Notice how each pair has a zero element next to it. The results are correct, but they are not in the correct order. In this example, you used **`vmovl`** to zero-extend values, which achieves the correct order with zero elements in place. While both **`vmovl`** and **`zip`** can be used for this purpose, **`vmovl`** was chosen in this implementation. For more details, see the Arm Software Optimization Guides, such as the [Neoverse V2 guide](https://developer.arm.com/documentation/109898/latest/).
 

From 77cb457c8b4d89f0695cf5c1dc30deca05911359 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 26 Nov 2024 15:37:04 +0000
Subject: [PATCH 50/87] Final editorial review.

---
 .../learning-paths/cross-platform/simd-info-demo/conclusion.md  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
index d16f95a0aa..14538a712f 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/conclusion.md
@@ -12,7 +12,7 @@ Porting SIMD code between architectures can be a daunting process, often requiri
 
 The primary focus of this Learning Path is to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. While reordering data to align with native Arm instructions can offer performance benefits, this is outside the scope of this Learning Path. 
 
-If you are interested in data layout strategies to further enhance performance on Arm, see the Learning Path *Optimize SIMD code with vectorization-friendly data layout* linked to in the **Next Steps** section at the end. This provides valuable insights.
+If you are interested in data layout strategies to further enhance performance on Arm, see the Learning Path *Optimize SIMD code with vectorization-friendly data layout* linked to in the **Next Steps** section at the of this Learning Path. 
 
 Using SIMD.info can be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding NEON equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.
 

From 4b9b61d233a73d19999940402ae3f69049e7c08e Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 26 Nov 2024 15:44:47 +0000
Subject: [PATCH 51/87] Update capitalization on Windows PowerShell

---
 .wordlist.txt                                                 | 1 -
 .../laptops-and-desktops/llvm_putty/putty_llvm.md             | 4 ++--
 .../laptops-and-desktops/win-opencv/2-2-build-clang.md        | 2 +-
 .../win_sandbox_dot_net_cicd/win_sandbox_net.md               | 2 +-
 content/learning-paths/laptops-and-desktops/wsl2/setup.md     | 2 +-
 content/learning-paths/laptops-and-desktops/wsl2/systemd.md   | 2 +-
 .../2-build-onnxruntime.md                                    | 2 +-
 .../3-build-onnxruntime-generate-api.md                       | 2 +-
 8 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/.wordlist.txt b/.wordlist.txt
index c5ade02d65..47fe254fa8 100644
--- a/.wordlist.txt
+++ b/.wordlist.txt
@@ -577,7 +577,6 @@ Pmod
 PostgerSQL
 PostgrSQL
 Postgres
-Powershell
 Prefetching
 Prereqs
 ProjectExplorer
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md
index 62a8f46aae..57caf69c84 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md
@@ -27,9 +27,9 @@ You can use Clang to build a CMake application for Windows on Arm. The example a
 
 To compile PuTTY on a Windows on Arm device, follow the steps outlined below:
 
-1. Open `Windows Powershell` on your Windows on Arm computer.
+1. Open `Windows PowerShell` on your Windows on Arm computer.
 
-Run the next two steps at the Powershell command prompt. 
+Run the next two steps at the PowerShell command prompt. 
 
 2. Create a directory to use for the build and go to the directory:
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
index a4d8e7cc70..f3311710da 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/2-2-build-clang.md
@@ -12,7 +12,7 @@ Follow the steps below to build OpenCV and a test application using the library
 
 ### Clone the OpenCV repository
 
-Open up a Windows Powershell and checkout the source tree:
+Open up a Windows PowerShell and checkout the source tree:
 
 ```bash
 git clone https://github.com/opencv/opencv
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/win_sandbox_net.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/win_sandbox_net.md
index 0ab8e7f935..4b66c37413 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/win_sandbox_net.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/win_sandbox_net.md
@@ -50,7 +50,7 @@ You will see instructions displayed on this page. These are commands you now nee
 
 On your host machine, click on the Search bar and enter **Windows Sandbox**.
 
-In your running Windows Sandbox, open up Powershell. You will now copy and paste all the commands into Powershell.
+In your running Windows Sandbox, open up PowerShell. You will now copy and paste all the commands into PowerShell.
 
 The commands are also shown here for your convenience. To download the runner package:
 
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/setup.md b/content/learning-paths/laptops-and-desktops/wsl2/setup.md
index 7a3253d514..440f79da48 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/setup.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/setup.md
@@ -64,7 +64,7 @@ There are other Linux distributions available in the Microsoft Store. Make sure
 
 Another way to install Linux distributions is using the WSL command. 
 
-Open a Windows Powershell or Command Prompt and list the distributions available:
+Open a Windows PowerShell or Command Prompt and list the distributions available:
 
 ```cmd 
 wsl --list --online
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/systemd.md b/content/learning-paths/laptops-and-desktops/wsl2/systemd.md
index 3f655ee02b..36251b091e 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/systemd.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/systemd.md
@@ -20,7 +20,7 @@ Add the following lines to `/etc/wsl.conf`:
 systemd=true
 ```
 
-Open a Windows Command Prompt or Powershell. Run the following commands to terminate and restart the distribution:
+Open a Windows Command Prompt or PowerShell. Run the following commands to terminate and restart the distribution:
 
 ```cmd
 wsl --terminate Ubuntu-22.04
diff --git a/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/2-build-onnxruntime.md b/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/2-build-onnxruntime.md
index d6541e2bd6..93ff353ee3 100644
--- a/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/2-build-onnxruntime.md
+++ b/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/2-build-onnxruntime.md
@@ -15,7 +15,7 @@ ONNX Runtime is an open-source inference engine designed to accelerate the deplo
 
 ### Clone onnxruntime repo
 
-Open up a Windows Powershell and checkout the source tree:
+Open up a Windows PowerShell and checkout the source tree:
 
 ```bash
 cd C:\Users\$env:USERNAME
diff --git a/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/3-build-onnxruntime-generate-api.md b/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/3-build-onnxruntime-generate-api.md
index 4ca2983bec..0d7e417440 100644
--- a/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/3-build-onnxruntime-generate-api.md
+++ b/content/learning-paths/smartphones-and-mobile/build-android-chat-app-using-onnxruntime/3-build-onnxruntime-generate-api.md
@@ -18,7 +18,7 @@ You can learn more by reading the [ONNX Runtime generate() API page](https://onn
 
 
 ### Clone onnxruntime-genai repo
-Within your Windows Powershell prompt, checkout the source repo:
+Within your Windows PowerShell prompt, checkout the source repo:
 
 ```bash
 C:\Users\$env:USERNAME

From 31613ea07b08146c0f99d8a2812b7cc2c495fad5 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 26 Nov 2024 20:57:17 +0000
Subject: [PATCH 52/87] New install guide for Git on Windows

---
 content/install-guides/_images/git-woa.png  | Bin 0 -> 19574 bytes
 content/install-guides/_images/git2-woa.png | Bin 0 -> 75982 bytes
 content/install-guides/_images/git3-woa.png | Bin 0 -> 18351 bytes
 content/install-guides/_images/git4-woa.png | Bin 0 -> 37109 bytes
 content/install-guides/_images/git5-woa.png | Bin 0 -> 21142 bytes
 content/install-guides/git-woa.md           | 103 ++++++++++++++++++++
 6 files changed, 103 insertions(+)
 create mode 100644 content/install-guides/_images/git-woa.png
 create mode 100644 content/install-guides/_images/git2-woa.png
 create mode 100644 content/install-guides/_images/git3-woa.png
 create mode 100644 content/install-guides/_images/git4-woa.png
 create mode 100644 content/install-guides/_images/git5-woa.png
 create mode 100644 content/install-guides/git-woa.md

diff --git a/content/install-guides/_images/git-woa.png b/content/install-guides/_images/git-woa.png
new file mode 100644
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diff --git a/content/install-guides/git-woa.md b/content/install-guides/git-woa.md
new file mode 100644
index 0000000000..14c4899d09
--- /dev/null
+++ b/content/install-guides/git-woa.md
@@ -0,0 +1,103 @@
+---
+### Title the install tools article with the name of the tool to be installed
+### Include vendor name where appropriate
+title: Git for Windows on Arm
+
+### Optional additional search terms (one per line) to assist in finding the article
+additional_search_terms:
+- git
+- windows
+- woa
+- windows on arm
+- open source windows on arm
+
+### Estimated completion time in minutes (please use integer multiple of 5)
+minutes_to_complete: 10
+
+### Link to official documentation
+official_docs: https://git-scm.com/doc
+
+author_primary: Jason Andrews
+
+### PAGE SETUP
+weight: 1                       # Defines page ordering. Must be 1 for first (or only) page.
+tool_install: true              # Set to true to be listed in main selection page, else false
+multi_install: false            # Set to true if first page of multi-page article, else false
+multitool_install_part: false   # Set to true if a sub-page of a multi-page article, else false
+layout: installtoolsall         # DO NOT MODIFY. Always true for tool install articles
+---
+
+Git has native support for [Windows on Arm](https://learn.microsoft.com/en-us/windows/arm/overview). Starting with version 2.47.1, an official installer is available. 
+
+In addition to Windows laptops, Windows on Arm instances are available with Microsoft Azure. For further information, see [Deploy a Windows on Arm virtual machine on Microsoft Azure](/learning-paths/cross-platform/woa_azure/).
+
+## How do I download and install Git for Windows on Arm?
+
+Git releases are available in [GitHub releases](https://github.com/git-for-windows/git/releases/).
+
+Use a browser to download the desired release file. The Git releases for Windows on Arm have `arm64.exe` in the filename.
+
+You can also download from a Windows PowerShell with the following command:
+
+```command
+curl https://github.com/git-for-windows/git/releases/download/v2.47.1.windows.1/Git-2.47.1-arm64.exe -o Git-2.47.1-arm64.exe
+```
+
+Once you have downloaded Git, run the installer `.exe` file on a Windows on Arm machine. 
+
+The installer starts. 
+
+Click Next to acknowledge the GNU General Public License.
+
+Set the destination location or accept the default location and click Next.
+
+Continue to click Next for any configuration settings. You can accept all defaults if you are unsure of specific settings.
+
+At the end of the install process you see the screen below indicating setup has finished installing Git:
+
+![Install](/install-guides/_images/git-woa.png)
+
+Click the Finish button to complete installation. 
+
+## How do I use Git on Windows? 
+
+You can use Git on Windows from a Command Prompt or using Git Bash, a Linux-like terminal experience which includes Git and many other Linux commands. 
+
+{{% notice Note %}}
+Git is not automatically added to your search path during installation. 
+{{% /notice %}}
+
+To use Git, click the Windows Start button and then click `All apps`.
+
+You see the Git folder in the G section.
+
+![Start](/install-guides/_images/git2-woa.png)
+
+There are menu items for multiple ways to start Git. 
+
+## How can I use Git in a Windows Command Prompt?
+
+Start a Git Command Prompt by selecting `Git CMD` from the start menu.
+
+![CMD](/install-guides/_images/git3-woa.png)
+
+
+To see the help message enter:
+
+```cmd
+git help
+```
+
+You can use Git from this Command Prompt. 
+
+## How can I use Git with Git Bash?
+
+To use Git in a Linux-like environment, select `Git Bash` from the start menu.
+
+![CMD](/install-guides/_images/git4-woa.png)
+
+Click the colored icon on the top left corner of the Git Bash window and then click `Options` to change the appearance of the window, including colors, fonts, and font sizes. 
+
+![Options](/install-guides/_images/git5-woa.png)
+
+You are now ready to use Git on your Windows on Arm device. 

From b8347b3022e3a788fc4a28cc075ec05c73eb1f67 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Mon, 25 Nov 2024 14:12:59 +0100
Subject: [PATCH 53/87] Update YOLOv8 on Himax Learning Path

---
 .../microcontrollers/yolo-on-himax/_index.md  |  2 +-
 .../yolo-on-himax/how-to-1.md                 | 19 +++++-
 .../yolo-on-himax/how-to-2.md                 | 23 +++++--
 .../yolo-on-himax/how-to-3.md                 | 65 ++++++++++++++++---
 .../yolo-on-himax/how-to-4.md                 |  7 +-
 .../yolo-on-himax/how-to-5.md                 | 25 ++++---
 6 files changed, 108 insertions(+), 33 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 4a5167d254..1dcf71ef7a 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -6,7 +6,7 @@ minutes_to_complete: 90
 who_is_this_for: This is an introduction topic for beginners on how to run a computer vision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
 
 learning_objectives:
-    - Run a you-only-look-once (YOLO) computer vision model on the edge device
+    - Run a you-only-look-once (YOLO) object detection model on the edge device
     - Build the Himax Software Development Kit (SDK) and generate the firmware image file
     - Update the firmware on the edge device (Himax WiseEye2)
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
index 7af1eb6ceb..cd5ceb2f83 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
@@ -26,11 +26,15 @@ You should see an output like the following.
 Python 3.12.7
 ```
 
-Install `pip` with the following commands, and check the output to verify it's installed correctly.
+Install `pip` and `venv` with the following commands.
 
 ```bash
 sudo apt update
-sudo apt install python3-pip -y
+sudo apt install python3-pip python3-venv -y
+```
+
+check the output to verify `pip` is installed correctly.
+```
 pip3 --version
 ```
 
@@ -38,6 +42,15 @@ pip3 --version
 pip 24.2 from /<path-to>/pip (python 3.12)
 ```
 
+It is considered good practice to manage `pip` packages through a virtual environment. Create one with the steps below.
+
+```bash
+python3 -m venv $HOME/yolo-venv
+source $HOME/yolo-venv/bin/activate
+```
+
+Your terminal displays `(yolo-venv)` in the prompt indicating the virtual environment is active.
+
 You will need to have the git version control system installed. Run the command below to verify that git is installed on your system.
 
 ```bash
@@ -98,7 +111,7 @@ export PATH="$HOME/arm-gnu-toolchain-13.2.Rel1-x86_64-arm-none-eabi/bin/:$PATH"
 cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.3.rel1/binrel/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
-export PATH="$HOME/code/tmp/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi/bin/:$PATH"
+export PATH="$HOME/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi/bin/:$PATH"
 ```
 
 {{% notice %}}
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
index 714961dc13..6d5377363e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
@@ -6,11 +6,11 @@ weight: 3
 layout: learningpathall
 ---
 
-TODO short intro / framing?
+This section will walk you though the process of generating the firmware image file.
 
 ## Clone the Himax project
 
-Himax has set up a repository containing a few examples for the Seeed Grove Vision AI V2 board. By recursively cloning the Himax examples repo, git will include the necessary sub-repositories that have been configured for the project.
+Himax has set up a repository containing a few examples for the Seeed Grove Vision AI V2 board. It contains third-party software and scripts to build and flash the image with the object detection application. By recursively cloning the Himax examples repo, git will include the necessary sub-repositories that have been configured for the project.
 
 ```bash
 git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git
@@ -19,35 +19,46 @@ cd Seeed_Grove_Vision_AI_Module_V2
 
 ## Compile the firmware
 
-The make build tool is used to compile the source code. This should take up to 10 minutes depending on the number of CPU cores available.
+For the object detection to activate, you need to edit the project's `makefile`, located in the `EPII_CM55M_APP_S` directory.
 
 ```bash
 cd EPII_CM55M_APP_S
+```
+Open the file and scroll down until you find the `APP_TYPE` attribute. Update the value to `tflm_yolov8_od`.
+
+```output
+APP_TYPE = tflm_yolov8_od
+```
+Use the `make` build tool to compile the source code. This should take up to 10 minutes depending on the number of CPU cores available on your host machine. The result is an `.elf` file written to the directory below.
+
+```bash
 make clean
 make
 ```
 
 ## Generate the firmware image
 
-The examples repository contains scripts to generate the image.
+Copy the `.elf` file to the `input_case1_secboot` directory.
 
 ```bash
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ```
+The examples repository contains scripts to generate the image. Run the script corresponding to the OS of your host machine.
 
-## Linux
+### Linux
 
 ```bash
 ./we2_local_image_gen project_case1_blp_wlcsp.json
 ```
 
-## macOS
+### macOS
 ```console
 ./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
 ```
 
 Your terminal output should end with the following.
+
 ```output
 Output image: output_case1_sec_wlcsp/output.img
 Output image: output_case1_sec_wlcsp/output.img
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
index 06d110108b..67a3f530ab 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
@@ -1,16 +1,16 @@
 ---
 title: Flash firmware onto the microcontroller
-weight: 3
+weight: 4
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-Now that we have generated a firmware file on our local machine, we need to flash the microcontroller with this firmware.
+Now that you have generated an image file on the local host machine, you are ready to flash the microcontroller with this firmware.
 
 ## Install xmodem
 
-`Xmodem` is a basic file transfer protocol. Run the following command to install the dependencies for xmodem. If you cloned the repository to a different location replace $HOME with the path.
+`Xmodem` is a basic file transfer protocol which is easily installed using the Himax examples repository. Run the following command to install the dependency. If you cloned the repository to a different location, replace $HOME with the path.
 
 ```bash
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2
@@ -19,7 +19,7 @@ pip install -r xmodem/requirements.txt
 
 ## Connect the module
 
-Insert the FPC cable cable into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
+Insert the Flexible printed circuit (FPC) into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
 
 ![unlatched](./unlatched.jpg)
 
@@ -29,20 +29,67 @@ Then, slide the FPC connector in with the metal pins facing down and close the d
 
 Then connect the Groove Vision AI V2 Module to your computer via the USB-C cable.
 
-### Flash the firmware onto the module
+{{% notice Note %}}
+The development board may have two USB-C connectors. If you are running into issues connecting the board in the next step, make sure you are using the right one.
+{{% /notice %}}
 
-Run the python script below to flash the firmware.
+## Find the COM port
+
+You'll need to provide the communication port (COM) which the board is connected to in order to flash the image. There are commands to list all COMs available on your machine. Once your board is connected through USB, it'll show up in this list. The COM identifier will start with **tty**, which may help you determine which one it is. You can run the command before and after plugging in the board if you are unsure.
+
+### Linux
 
 ```bash
-python xmodem\xmodem_send.py --port=[your COM number] --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+sudo grep -i 'tty' /var/log/dmesg
+```
+
+### MacOS
+
+```console
+ls /dev/tty.*
 ```
 
 {{% notice Note %}}
-When you run other example models demonstrated in the later section [Object detection and additional models](/learning-paths/microcontrollers/yolo-on-himax/how-to-5/), you need to adapt this command.
+If the port seems unavailable, try changing the permissions temporarily using the `chmod` command. Be sure to reset them afterwards, as this may pose a computer security vulnerability.
+
+```bash
+chmod 0777 <COM port>
+```
 {{% /notice %}}
 
-TODO: how will the command really change? How to find COM number?
+The full path to the port is needed in the next step, so be sure to note it down.
+
+## Flash the firmware onto the module
+
+Run the python script below to flash the firmware.
+
+```bash
+python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+```
+
+{{% notice Note %}}
+When you run other example models demonstrated in the later section [Object detection and additional models](/learning-paths/microcontrollers/yolo-on-himax/how-to-5/), you need to adapt this command with the right image file.
+{{% /notice %}}
 
 After the firmware image burning is completed, the message `Do you want to end file transmission and reboot system? (y)` is displayed. Press the reset button indicated in the image below.
 
 ![reset button](./reset_button.jpg)
+
+## Run the model
+
+After the reset button is pressed, the board will start inference with the object detection automatically. Observe the output in the terminal to verify that the image is built correctly. If a person is in front of the camera, you should see the `person_score` value go over `100`.
+
+```output
+b'SENSORDPLIB_STATUS_XDMA_FRAME_READY 240'
+b'write frame result 0, data size=15284,addr=0x340e04e0'
+b'invoke pass'
+b'person_score:113'
+b'EVT event = 10'
+b'SENSORDPLIB_STATUS_XDMA_FRAME_READY 241'
+b'write frame result 0, data size=15296,addr=0x340e04e0'
+b'invoke pass'
+b'person_score:112'
+b'EVT event = 10'
+```
+
+You have now verified that the model works correctly in the firmware. The next step is to view it using a web toolkit.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
index 522f23f29b..b34e3e9e5c 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
@@ -1,15 +1,12 @@
 ---
 title: Run and view model results
-weight: 3
+weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-
-## Connect the board with USB cable
-
-Exit the terminal session and connect the module to your host machine using the USB-C cable.
+In this section, you will view a live camera feed with the ML application running.
 
 ## Download the Himax AI web toolkit
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
index 9f3cfde92d..bc84d98418 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
@@ -1,26 +1,22 @@
 ---
 title: Object detection and additional models
-weight: 5
+weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-TODO some more intro here, and showing how to test the model
-Also double check this section
+There are other computer vision applications to try. In this section, you will re-flash the module with a different one and check the results.
 
 ## Modify the Makefile
 
-TODO: why are we doing this?
-
 Change the directory to the where the Makefile is located. If you cloned the repository to a different location, replace $HOME with the path.
 
 ```bash
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
 ```
 
-Modify the `APP_TYPE` field in the Makefile from the default value of `allon_sensor_tflm` to one of the values in the table below
-
+The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the Makefile to one of the values in the table.
 
 |APP_TYPE =|Description|
 |---|---|
@@ -28,11 +24,22 @@ Modify the `APP_TYPE` field in the Makefile from the default value of `allon_sen
 |tflm_folov8_pose|Pose detection|
 |tflm_fd_fm|Face detection|
 
+
 ## Regenerate the firmware image
 
-Go back to the [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/how-to-3/) section and run the python command to regenerate the firmware image.
+Now you can run `make` to re-generate the `.elf` file.
+
+```bash
+make clean
+make
+```
+Use the command from [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/how-to-3/) section to run re-generate the firmware image.
+
+```bash
+python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+```
 
-The images below are examples images from the model.
+The images below are captured images from the models run in the toolkit.
 
 ### Objection detection
 ![object_detection](./object_detection.jpg)

From 28583748000033dbfb5642eeb23101226fea0c2c Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Wed, 27 Nov 2024 15:16:49 +0000
Subject: [PATCH 54/87] Editorial review

---
 .../1-webgpu-fundamentals.md                  | 32 +++++++++----------
 .../android_webgpu_dawn/_index.md             | 15 +++++----
 2 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
index 0ccb2f79c4..0ffacf456d 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
@@ -1,5 +1,5 @@
 ---
-title: Introduction to WebGPU
+title: Overview of WebGPU
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -8,43 +8,43 @@ layout: learningpathall
 
 ## What is WebGPU?
 
-WebGPU is the successor to WebGL, a well adopted modern API standard for interfacing with GPUs. WebGPU provides better compatibility with modern GPUs, support for general-purpose GPU computations, faster operations, and access to more advanced GPU features. It is designed to provide a _unified access_ to GPUs, agnostic to GPU vendors and operating systems.
+WebGPU is the successor to WebGL, a well-adopted modern API standard for interfacing with GPUs. WebGPU provides better compatibility with modern GPUs, support for general-purpose GPU computations, faster operations, and access to more advanced GPU features. It is designed to provide a _unified access_ to GPUs, agnostic to GPU vendors and operating systems.
 
-WebGPU is a Render Hardware Interface built on top of various backend APIs like Vulkan, DirectX, and Metal (depending on the operating system). 
+WebGPU is a Render Hardware Interface built on top of various backend APIs like Vulkan, DirectX, and Metal - though this depends on the operating system. 
 
 WebGPU is available through web browsers using the webgpu.h header file. 
 
-The high level view of WebGPU is shown below:
+The high-level view of WebGPU is shown below in Figure 1:
 
-![WebGPU high level view #center](images/webgpu_highlevel.png "WebGPU High Level View")
+![Figure 1: WebGPU high level view #center](images/webgpu_highlevel.png "Figure 1: WebGPU High-Level View")
 
 ## What are the benefits of WebGPU?
 
-WebGPU takes into account learnings from older standards like WebGL and OpenGL and provides the following benefits: 
+WebGPU takes into account lessons learned from older standards like WebGL and OpenGL, and provides the following benefits: 
 
-* A reasonable level of abstraction
-* Good performance
-* Cross-platform
-* Backed by W3C standards group
-* Future-proof design
+* A reasonable level of abstraction.
+* Improved performance.
+* Cross-platform functionality.
+* Backing from the W3C standards group.
+* A future-proof design.
 
-WebGPU is a standard and not a true API, so the implementation can be adopted and developed as an interface between native applications developed in any programming language.
+WebGPU is a standard, and not a true API, so the implementation can be adopted and developed as an interface between native applications developed in any programming language.
 
-The performance requirements for web pages is actually the same as for native application.
+The performance requirements for web pages are the same as for native application.
 
 {{% notice Note %}}
 When designing an API for the Web, the two key constraints are portability and privacy. 
 
-The limitations of the API due to privacy considerations can be disabled when using WebGPU as a native API.
+Any limitations of the API caused by privacy settings can be disabled when using WebGPU as a native API.
 {{% /notice %}}
 
 ## What are the benefits of using C++ for WebGPU?
 
 The initial target for WebGPU was JavaScript.  The initial `webgpu.h` header file is written in C. 
 
-This Learning Path uses C++ rather than JavaScript or C because for the following reasons:
+This Learning Path uses C++ rather than JavaScript or C for the following reasons:
 
-* C++ is still the primary language used for high performance graphics applications, such as video games, render engines, and modeling tools.
+* C++ remains the primary language used for high performance graphics applications, such as video games, render engines, and modeling tools.
 * The level of abstraction and control of C++ is well suited for interacting with graphics APIs in general.
 * Graphics programming is a good way to learn more C++.
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index f1a307a26b..bccf38cc60 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -1,26 +1,27 @@
 ---
 title: Build and profile a simple WebGPU Android Application
 
-draft: true
+
 cascade:
-    draft: true
+
 
 minutes_to_complete: 90
 
-who_is_this_for: This is an introductory topic for developers building GPU based Android applications and interested in trying WebGPU. 
+who_is_this_for: This is an introductory topic for developers who are building GPU-based Android applications and are interested in trying WebGPU. 
 
 learning_objectives: 
-    - Understand the benefits of WebGPU and Dawn, a WebGPU implementation.
+    - Describe the benefits of WebGPU.
+    - Describe the benefits of using Dawn.
     - Set up a WebGPU development environment.
     - Integrate Dawn in an Android Application.
     - Use Dawn WebGPU APIs in the application.
-    - Understand the changes required to upgrade to WebGPU to render a simple 3D object.
+    - Describe the changes required to upgrade to WebGPU to render a simple 3D object.
     - Build and run a WebGPU Android Application.
-    - Profile the Application using Streamline.
+    - Profile the application using Streamline.
     - Analyze the profiling data.
     
 prerequisites:
-    - Basic knowledge of graphics APIs and experience with developing Android graphics applications.
+    - Basic knowledge of graphics APIs and experience in developing Android graphics applications.
     - A development machine with Android Studio, Blender, and Arm Streamline installed.
     - An Android phone in developer mode.
 

From cf78c9017d7fe645de972a13f3085c5494743ec1 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Thu, 28 Nov 2024 15:54:30 +0000
Subject: [PATCH 55/87] Editorial.

---
 .../1-webgpu-fundamentals.md                  | 30 ++++++++++---------
 .../android_webgpu_dawn/2-env-setup.md        | 10 +------
 .../android_webgpu_dawn/_index.md             |  9 +++---
 3 files changed, 21 insertions(+), 28 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
index 0ffacf456d..d337d0296d 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
@@ -8,9 +8,11 @@ layout: learningpathall
 
 ## What is WebGPU?
 
-WebGPU is the successor to WebGL, a well-adopted modern API standard for interfacing with GPUs. WebGPU provides better compatibility with modern GPUs, support for general-purpose GPU computations, faster operations, and access to more advanced GPU features. It is designed to provide a _unified access_ to GPUs, agnostic to GPU vendors and operating systems.
+WebGPU is the successor to WebGL, a well-adopted modern API standard for interfacing with GPUs. 
 
-WebGPU is a Render Hardware Interface built on top of various backend APIs like Vulkan, DirectX, and Metal - though this depends on the operating system. 
+WebGPU offers improved compatibility with modern GPUs, support for general-purpose GPU computations, faster operations, and access to more advanced GPU features. It is designed to provide a unified access to GPUs, that is both GPU vendor-agnostic and OS-agnostic.
+
+WebGPU is a Render Hardware Interface built on top of various backend APIs such as Vulkan, DirectX, and Metal - though this does depend on the operating system. 
 
 WebGPU is available through web browsers using the webgpu.h header file. 
 
@@ -20,7 +22,7 @@ The high-level view of WebGPU is shown below in Figure 1:
 
 ## What are the benefits of WebGPU?
 
-WebGPU takes into account lessons learned from older standards like WebGL and OpenGL, and provides the following benefits: 
+WebGPU takes into account lessons learned from earlier standards such as WebGL and OpenGL, and provides the following benefits: 
 
 * A reasonable level of abstraction.
 * Improved performance.
@@ -28,41 +30,41 @@ WebGPU takes into account lessons learned from older standards like WebGL and Op
 * Backing from the W3C standards group.
 * A future-proof design.
 
-WebGPU is a standard, and not a true API, so the implementation can be adopted and developed as an interface between native applications developed in any programming language.
+It is important to note that WebGPU is a standard, and not a true API, so the implementation can be adopted and developed as an interface between native applications developed in any programming language.
 
-The performance requirements for web pages are the same as for native application.
+The performance requirements for web pages are the same as for native applications.
 
 {{% notice Note %}}
 When designing an API for the Web, the two key constraints are portability and privacy. 
 
-Any limitations of the API caused by privacy settings can be disabled when using WebGPU as a native API.
+Any limitations of the API caused by privacy constraints can be disabled when using WebGPU as a native API.
 {{% /notice %}}
 
 ## What are the benefits of using C++ for WebGPU?
 
-The initial target for WebGPU was JavaScript.  The initial `webgpu.h` header file is written in C. 
+The initial focus for WebGPU was JavaScript.  The initial `webgpu.h` header file is written in C. 
 
 This Learning Path uses C++ rather than JavaScript or C for the following reasons:
 
 * C++ remains the primary language used for high performance graphics applications, such as video games, render engines, and modeling tools.
 * The level of abstraction and control of C++ is well suited for interacting with graphics APIs in general.
-* Graphics programming is a good way to learn more C++.
+* Graphics programming is a good way to improve skills in C++.
 
 ## Dawn: the Google WebGPU implementation
 
-Since WebGPU is a standard and not an implementation, there are different implementations. 
+Since WebGPU is a standard and not an implementation itself, there are various possible implementations. 
 
 [Dawn](https://github.com/google/dawn) is an open-source, cross-platform implementation of the WebGPU standard. 
 
-It implements the WebGPU functionality specified in `webgpu.h`. Dawn is meant to be integrated as part of a larger system like Chromium or a native Android Application.
+It implements the WebGPU functionality specified in `webgpu.h`. Dawn is designed to be integrated as part of a larger system such as Chromium or a native Android Application.
 
 Dawn provides several WebGPU building blocks:
 
 * WebGPU C/C++ headers that applications and other building blocks use, including a header file and C++ wrapper.
-* A "native" implementation of WebGPU using appropriate APIs: D3D12, Metal, Vulkan and OpenGL. 
+* A "native" implementation of WebGPU using appropriate APIs: D3D12, Metal, Vulkan, and OpenGL. 
 * A client-server implementation of WebGPU for applications that are in a sandbox without access to native drivers.
-* Tint, a compiler for the WebGPU Shader Language (WGSL), that converts shaders to and from WGSL.
+* Tint; a compiler for the WebGPU Shader Language (WGSL), that converts shaders to and from WGSL.
 
-Because it is written in C++, Dawn provides better error messages and logging. Because it is open-source, it is easier to inspect stack traces when applications crash.
+Because it is written in C++, Dawn provides enhanced error message reporting and logging. The fact that it is open source, also means that it is easier to inspect stack traces when applications crash.
 
-Dawn is usually ahead of `wgpu-native`, another WebGPU implementation, when it comes to new functionalities and standards changes. 
+Dawn is usually ahead of `wgpu-native`, another WebGPU implementation, in terms of new functionality developments and standards changes. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
index 070d2456c2..4853960534 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
@@ -6,17 +6,9 @@ weight: 3
 layout: learningpathall
 ---
 
-In this Learning Path, you will learn how to:
-
-* Integrate Dawn (WebGPU) in an application.
-* Use the APIs to render a simple 3D object.
-* Profile and analyze the application.
-
 The first step is to prepare a development environment with the required software:
 
-* [Android Studio](https://developer.android.com/studio)
-* [Arm Performance Studio](https://www.arm.com/products/development-tools/graphics/arm-performance-studio) 
-* Python 3.10 or later
+
 
 You can use any computer and operating system which supports the above software. 
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index bccf38cc60..8c2d6f4908 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -1,10 +1,6 @@
 ---
 title: Build and profile a simple WebGPU Android Application
-
-
 cascade:
-
-
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for developers who are building GPU-based Android applications and are interested in trying WebGPU. 
@@ -19,11 +15,14 @@ learning_objectives:
     - Build and run a WebGPU Android Application.
     - Profile the application using Streamline.
     - Analyze the profiling data.
-    
+       
 prerequisites:
     - Basic knowledge of graphics APIs and experience in developing Android graphics applications.
     - A development machine with Android Studio, Blender, and Arm Streamline installed.
     - An Android phone in developer mode.
+    - [Android Studio](https://developer.android.com/studio)
+    - [Arm Performance Studio](https://www.arm.com/products/development-tools/graphics/arm-performance-studio) 
+    - Python 3.10 or later
 
 author_primary: Varun Chari, Albin Bernhardsson
 

From 15f8dd327e3a1b7255368c19d16d233254e7462c Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Thu, 28 Nov 2024 16:08:26 +0000
Subject: [PATCH 56/87] Editorial.

---
 .../smartphones-and-mobile/android_webgpu_dawn/_index.md      | 4 ++--
 .../smartphones-and-mobile/android_webgpu_dawn/_review.md     | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index 8c2d6f4908..0b153fe46c 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -20,8 +20,8 @@ prerequisites:
     - Basic knowledge of graphics APIs and experience in developing Android graphics applications.
     - A development machine with Android Studio, Blender, and Arm Streamline installed.
     - An Android phone in developer mode.
-    - [Android Studio](https://developer.android.com/studio)
-    - [Arm Performance Studio](https://www.arm.com/products/development-tools/graphics/arm-performance-studio) 
+    - Android Studio
+    - Arm Performance Studio
     - Python 3.10 or later
 
 author_primary: Varun Chari, Albin Bernhardsson
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
index f12277b62d..6d4d9d6d3a 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
@@ -4,7 +4,7 @@ review:
         question: >
             What is WebGPU?
         answers:
-            - A highly customized API for specific GPUs.
+            - A highly-customized API for specific GPUs.
             - APIs designed to provide unified access to GPUs whichever the GPU vendor and operating system the application runs with.
             - APIs designed for cloud-based applications.
         correct_answer: 2                    
@@ -28,7 +28,7 @@ review:
         answers:
             - A profiling tool to profile CPUs.
             - A profiling tool to profile GPUs.
-            - A a comprehensive profiling software to profile both CPUs and GPUs.
+            - A comprehensive profiling software to profile both CPUs and GPUs.
         correct_answer: 3          
         explanation: >
             Streamline is an application profiler that can capture data from multiple sources, including Program Counters (PC), Samples from the hardware Performance Monitoring Unit (PMU) counters in the Arm CPU, Arm® Mali™ GPUs, and Arm Immortalis™ GPUs.

From dd14fb6e21382df9c9daa25fa74038b144b84bac Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Thu, 28 Nov 2024 17:17:48 +0000
Subject: [PATCH 57/87] Editorial review.

---
 .../android_webgpu_dawn/2-env-setup.md        | 36 +++++++------
 .../android_webgpu_dawn/3-integrate-dawn.md   | 52 ++++++++++---------
 .../4-using-webgpu-apis.md                    |  6 +--
 .../7-Profiling-App-using-Streamline.md       | 34 ++++++------
 .../android_webgpu_dawn/_index.md             |  6 +--
 5 files changed, 69 insertions(+), 65 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
index 4853960534..3f566eeda7 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
@@ -8,9 +8,11 @@ layout: learningpathall
 
 The first step is to prepare a development environment with the required software:
 
+* Android Studio.
+* Arm Performance Studio.
+* Python 3.10 or later.
 
-
-You can use any computer and operating system which supports the above software. 
+You can use any computer and operating system that supports this software. 
 
 ## Install Android Studio and the Android NDK
 
@@ -18,28 +20,28 @@ You can use any computer and operating system which supports the above software.
 
 2. Start Android Studio.
 
-3. Open the `Settings` dialog.
+3. Open the **Settings** dialog.
 
-4. Navigate to `Languages & Frameworks`, then select `Android SDK`.
+4. Navigate to **Languages & Frameworks**, then select **Android SDK**.
 
-5. In the `SDK Platforms` tab, check `Android 14.0 ("UpsideDownCake")`
+5. In the **SDK Platforms** tab, select the checkbox **Android 14.0** ("UpsideDownCake").
 
-![SDK Platforms #center](images/sdk-platforms.png "SDK Platforms")
+![SDK Platforms #center](images/sdk-platforms.png "Figure 2: SDK Platforms")
 
-6. In the `SDK Tools` tab check the following:
-    * Check `Android SDK Build-Tools 35`
-    * Check `NDK (Side by side)`
-    * Check `CMake`
+6. In the **SDK Tools** tab, select the following:
+    * Select **Android SDK Build-Tools 35**.
+    * Select **NDK (Side by side)**.
+    * Select **CMake**.
 
-![SDK Tools #center](images/sdk-tools.png "SDK Tools")
+![SDK Tools #center](images/sdk-tools.png "Figure 3: SDK Tools")
 
-Click OK to install and update the selected components. 
+Click **OK** to install and update the selected components. 
 
 ## Install Arm Performance Studio
 
 Profiling is an important step in the Android application development cycle. 
 
-The default profiler in the Android Studio is great to profile CPU related metrics, but does not provide GPU details. 
+The default profiler in Android Studio is great to profile CPU-related metrics, but does not provide GPU details. 
 
 Arm Performance Studio is a comprehensive profiling tool to profile both CPUs and GPUs. 
 
@@ -47,13 +49,13 @@ One of the components of Performance Studio is Streamline. Streamline captures d
 
 * Program Counter (PC) samples from running application threads.
 * Samples from the hardware Performance Monitoring Unit (PMU) counters in Arm CPUs, Arm Mali GPUs, and Arm Immortalis GPUs.
-* Thread scheduling information from the Linux kernel.
+* Thread-scheduling information from the Linux kernel.
 * Software-generated annotations and counters from running applications.
 
-Install Arm Performance Studio using the [install guide](/install-guides/ams/).
+Install Arm Performance Studio using the [Arm Performance Studio Install Guide](/install-guides/ams/).
 
 {{% notice Tip %}}
-If you want to learn more about Arm Performance Studio and Streamline before continuing, refer to [Get started with Arm Performance Studio for mobile](https://learn.arm.com/learning-paths/smartphones-and-mobile/ams/ams/)
+If you want to learn more about Arm Performance Studio and Streamline before continuing, refer to [Get started with Arm Performance Studio for mobile](https://learn.arm.com/learning-paths/smartphones-and-mobile/ams/ams/).
 {{% /notice %}}
 
-Android Studio and Arm Performance Studio are now installed and you are ready to create a WebGPU Android application. 
+Android Studio and Arm Performance Studio are now installed, and you are ready to create a WebGPU Android application. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
index 8b487e4bb7..4228762ef0 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
@@ -1,5 +1,5 @@
 ---
-title: Create an application which includes Dawn
+title: Create an application with Dawn
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -8,17 +8,17 @@ layout: learningpathall
 
 ## Set up Android Project
 
-You can start by creating a new Android Studio project.
+Start by creating a new Android Studio project.
 
-Open Android studio, click `New Project` and select `Game Activity (C++)` as shown below:
+Open Android Studio, click **New Project** and select **Game Activity (C++)** as shown below:
 
-![New Game Activity #center](./images/android_studio_new_game_activity.png "New C++ Game Activity")
+![New Game Activity #center](./images/android_studio_new_game_activity.png "Figure 4: New C++ Game Activity")
 
-Set the `Name` to be `dawnwebgpu`. 
+Set the **Name** field to **dawnwebgpu**. 
 
-Select `Next` to continue. 
+Click **Next** to continue. 
 
-Finish the new project creation by accepting all defaults until the project is created. 
+Finish the new project creation by accepting all the default selections until the project is created. 
 
 The project is created in `~/AndroidStudioProjects`.
 
@@ -28,15 +28,15 @@ GameActivity is a Jetpack library designed to assist Android games in processing
 
 GameActivity is a direct descendant of NativeActivity and shares a similar architecture:
 
-![Game Activity Architecture #center](./images/GameActivityArchitecture.png "Game Activity Architecture")
+![Game Activity Architecture #center](./images/GameActivityArchitecture.png "Figure 5: Game Activity Architecture")
 
-With GameActivity, you can focus on your game development and avoid spending excessive time dealing with the Java Native Interface (JNI) code.
+With GameActivity, you can focus on game development and avoid spending excessive amounts of time dealing with the Java Native Interface (JNI) code.
 
 GameActivity performs the following functions:
 
-* Interacts with the Android framework through the Java-side component.
-* Passes app cycle commands, input events, and input text to the native side.
-* Renders into a SurfaceView, making it easier for games to interact with other UI components.
+* It interacts with the Android framework through the Java-side component.
+* It passes app cycle commands, input events, and input text to the native side.
+* It renders into a SurfaceView, making it easier for games to interact with other UI components.
 
 {{% notice Tip %}}
 You can find more information about Android Game Activity and its capabilities in the [Game Activity documentation](https://developer.android.com/games/agdk/game-activity).
@@ -44,7 +44,9 @@ You can find more information about Android Game Activity and its capabilities i
 
 ## Download project source files
 
-To create a WebGPU application, a number of files from GitHub are doing to be added to your Game Activity project. The objective is to show you how to take the Game Activity template and modify it to become a WebGPU application. 
+The process of creating a WebGPU application involves adding a number of files from GitHub to your Game Activity project. 
+
+The objective of this part of the Learning Path is to show you how to take the Game Activity template and modify it to become a WebGPU application. 
 
 To get started, open a terminal, create a new directory, and download the project files:
 
@@ -59,9 +61,9 @@ Unzip the project files:
 unzip main.zip
 ```
 
-Yow now have a directory named `Android_DawnWebGPU-main` in your `webgpu-files` directory. 
+You now have a directory named **Android_DawnWebGPU-main** in your **webgpu-files** directory. 
 
-During the next sections you will copy some of the required files from the `Android_DawnWebGPU-main` directory to your Game Activity project to learn how to create WebGPU applications.
+During the following sections you will copy some of the required files from the **Android_DawnWebGPU-main** directory into your Game Activity project to learn how to create WebGPU applications.
 
 ## Upgrade the application to include Dawn
 
@@ -73,13 +75,13 @@ You can remove this dependency and replace it with WebGPU.
 
 Add WebGPU to the project using the following steps:
 
-1. In Android Studio, navigate to the project view and find the `app` --> `cpp` folder.
+1. In Android Studio, navigate to the project view and find the **app** --> **cpp** folder.
 
-Open terminal in Android Studio. You should be in the `dawnwebgpu` directory.
+2. Open the terminal in Android Studio. You are now in the **dawnwebgpu** directory.
 
-2. Create a new directory and download the WebGPU header file from GitHub
+3. Create a new directory and download the WebGPU header file from GitHub.
 
-Run the commands below to download the `webgpu.hpp` header file:
+4. Run the commands below to download the `webgpu.hpp` header file:
 
 ```console
 mkdir -p app/src/main/cpp/webgpu/include/webgpu
@@ -88,7 +90,7 @@ cp ~/webgpu-files/Android_DawnWebGPU-main/app/src/main/cpp/webgpu/include/webgpu
 cd ../..
 ```
 
-3. Next copy the remaining WebGPU files to your project. 
+5. Next copy the remaining WebGPU files to your project. 
 
 ```console
 cp ~/webgpu-files/Android_DawnWebGPU-main/app/src/main/cpp/webgpu/CMakeLists.txt .
@@ -101,15 +103,15 @@ cd ..
 Notice that `FetchDawn.cmake` uses a stable `chromium/6536` branch of Dawn repository. 
 
 {{% notice Note %}}
-WebGPU is constantly evolving standard and hence its implementation, Dawn is also under active development. For sake of stability, we have chosen a stable branch for our development. Updating to latest or different branch may cause breakage.
+WebGPU is a constantly evolving standard and hence its implementation, Dawn is also under active development. For sake of stability, we have chosen a stable branch for our development. Updating to latest or different branch may cause breakage.
 {{% /notice %}}
 
-To add Dawn to our application, there are 2 options:
+To add Dawn to our application, there are two options:
 
-* Create a shared/static library from the Dawn source and use it in application.
-* Download the source as a dependency and build it as part of the project build.
+1. Create a shared/static library from the Dawn source and use it in application.
+2. Download the source as a dependency and build it as part of the project build.
 
-You will use the second option, since it provides more debug flexibility.
+You will use the second option here, since it provides more flexibility for debug.
 
 The files `webgpu/webgpu.cmake` and `CMakeLists.txt` facilitate downloading and building WebGPU with Dawn implementation and integrating Dawn into the project.
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
index a3acd342c2..1e4c9eacfc 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
@@ -6,7 +6,7 @@ weight: 5
 layout: learningpathall
 ---
 
-## Setup project
+## Project Setup
 
 With the `webgpudawn` library integrated, you can start by removing the extra files included as part of the stock Game Activity project. 
 
@@ -34,7 +34,7 @@ With the `webgpudawn` library integrated, you can start by removing the extra fi
 
 There are several layers of abstraction between a device GPU and an application running the WebGPU API.
 
-![WebGPU Application Interface #center](images/webgpu_app_interface.png "WebGPU Application Interface")
+![WebGPU Application Interface #center](images/webgpu_app_interface.png "Figure 6: WebGPU Application Interface")
 
 It is useful to understand these layers as you begin to use WebGPU APIs in an application.
 
@@ -56,7 +56,7 @@ These are used to determine the overall capabilities of the system before **requ
 
 Once a code path is chosen, a device is created with the chosen capabilities. Only the capabilities selected for this device are  allowed in the rest of the application. This way, it is **not** possible to inadvertently rely on capabilities specific to a device.
 
-![Supported Limits #center](images/adapter_supported_limits.png "Adapter Supported Limits")
+![Supported Limits #center](images/adapter_supported_limits.png "Figure 7: Adapter Supported Limits")
 
 {{% notice Tip %}}
 In an advanced use of the adapter/device duality, you can set up multiple limit presets and select one depending on the adapter. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index 583395c6b4..273c619fab 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -6,54 +6,54 @@ weight: 8
 layout: learningpathall
 ---
 
-## Setup Arm Streamline
+## Set up Arm Streamline
 
 Follow these steps to configure Arm Streamline Performance Analyzer to capture Mali GPU related data:
 
-* Confirm you Android device is connected to the development machine.
-* Navigate to **Start tab**, select **Android (adb)**, select the device and then select the application to debug.
-* Click on **Select Counters**
+* Confirm your Android device is connected to the development machine.
+* Navigate to the **Start** tab, select **Android (adb)**, select the device, and then select the application to debug.
+* Select **Select Counters**.
 
 ![Select device #center](images/streamline_select.png "Select device")
 
-which opens a new window:
+This opens a new window:
 
 ![Select counters #center](images/streamline_select_counters.png "Select counters")
 
-* Search for **Mali Timeline Events: Perfetto**
-* Make sure it is listed in the **Events to collect**
-* Click Save
+* Search for **Mali Timeline Events: Perfetto**.
+* Check that it is listed in **Events to collect**.
+* Select **Save**.
 
 ## Profiling the application using Streamline
 
-Once you have selected the device, the application and metrics to be collected, click on the **start capture** button. 
+Once you have selected the device, the application, and the metrics to be collected, click on the **start capture** button. 
 
 This automatically starts the application and begins collecting the profiling data. 
 
-Make sure the application is running as desired on your Android device. After a few seconds, you can stop the capture. 
+Make sure the application is running correctly on your Android device. After a few seconds, you can stop the capture. 
 
 Wait until Streamline completes processing the data. 
 
-Switch to *Mali Timeline* view as shown below:
+Switch to **Mali Timeline** view as shown below:
 
 !["Mali Timeline Streamline" #center](images/Streamline-mali-timeline.png "Mali Timeline Streamline")
 
-You may have to zoom into the data to the maximum (`500 us`), since you are rendering a very simple 3D object. 
+You might have to zoom into the data up to the maximum (**500 us**), as you are rendering a very simple 3D object. 
 
-You can analyze 2 consecutive frames as shown below:
+You can analyze two consecutive frames as shown below:
 
 !["Two consecutive frames" #center](./images/Streamline-mali-analysis.png "Two consecutive frames")
 
 Arm has worked with the Dawn team to optimize data uploading to GPU buffers for Mali GPUs. 
 
-Arm has implemented a **Fast Path** mechanism wherein the Vertex Queue starts processing in parallel while an earlier Fragment Queue is being processed. 
+Arm has implemented a **Fast Path** mechanism where the Vertex Queue starts processing in parallel while an earlier Fragment Queue is simultaneously being processed. 
 
 As you can see from the above picture, there is some overlap between Fragment Queue of first frame and Vertex Queue of the consecutive frame. 
 
-This shows that the application is hitting the **Fast Path** that Arm has implemented to optimize performance of Dawn for Mali GPUs. 
+This demonstrates that the application is hitting the **Fast Path** that Arm has implemented to optimize performance of Dawn for Mali GPUs. 
 
-The overlap is small since the application is rendering the same simple 3D object under different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This would show the overlap in more detail.
+The overlap is small since the application is rendering the same simple 3D object under different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This will demonstrate the overlap in more detail.
 
 {{% notice Tip %}}
-Feel free to experiment with different counters in Streamline and explore the other CPU profiling data as well.
+You can experiment with different counters in Streamline and also explore other CPU profiling data.
 {{% /notice %}}
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index 0b153fe46c..d6c17d25a6 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -20,9 +20,9 @@ prerequisites:
     - Basic knowledge of graphics APIs and experience in developing Android graphics applications.
     - A development machine with Android Studio, Blender, and Arm Streamline installed.
     - An Android phone in developer mode.
-    - Android Studio
-    - Arm Performance Studio
-    - Python 3.10 or later
+    - Android Studio.
+    - Arm Performance Studio.
+    - Python 3.10 or later.
 
 author_primary: Varun Chari, Albin Bernhardsson
 

From 8f790bfadd9c7832676944ebfe087c042121288f Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Thu, 28 Nov 2024 18:23:10 +0100
Subject: [PATCH 58/87] Update YOLOv8 on Himax Learning Path

---
 .../{how-to-2.md => build-firmware.md}        |  4 -
 .../yolo-on-himax/{how-to-1.md => dev-env.md} |  0
 .../{how-to-3.md => flash-and-run.md}         |  2 +-
 .../yolo-on-himax/how-to-4.md                 | 32 -------
 .../yolo-on-himax/how-to-5.md                 | 52 -----------
 .../yolo-on-himax/web-toolkit.md              | 92 +++++++++++++++++++
 6 files changed, 93 insertions(+), 89 deletions(-)
 rename content/learning-paths/microcontrollers/yolo-on-himax/{how-to-2.md => build-firmware.md} (92%)
 rename content/learning-paths/microcontrollers/yolo-on-himax/{how-to-1.md => dev-env.md} (100%)
 rename content/learning-paths/microcontrollers/yolo-on-himax/{how-to-3.md => flash-and-run.md} (96%)
 delete mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
 delete mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
 create mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
similarity index 92%
rename from content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
rename to content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
index 6d5377363e..d2f5604b99 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-2.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
@@ -24,11 +24,7 @@ For the object detection to activate, you need to edit the project's `makefile`,
 ```bash
 cd EPII_CM55M_APP_S
 ```
-Open the file and scroll down until you find the `APP_TYPE` attribute. Update the value to `tflm_yolov8_od`.
 
-```output
-APP_TYPE = tflm_yolov8_od
-```
 Use the `make` build tool to compile the source code. This should take up to 10 minutes depending on the number of CPU cores available on your host machine. The result is an `.elf` file written to the directory below.
 
 ```bash
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md b/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
similarity index 100%
rename from content/learning-paths/microcontrollers/yolo-on-himax/how-to-1.md
rename to content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
similarity index 96%
rename from content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
rename to content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
index 67a3f530ab..71e16134fd 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-3.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
@@ -92,4 +92,4 @@ b'person_score:112'
 b'EVT event = 10'
 ```
 
-You have now verified that the model works correctly in the firmware. The next step is to view it using a web toolkit.
\ No newline at end of file
+This means the image works correctly on the device, and the end-to-end flow is complete.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
deleted file mode 100644
index b34e3e9e5c..0000000000
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-4.md
+++ /dev/null
@@ -1,32 +0,0 @@
----
-title: Run and view model results
-weight: 5
-
-### FIXED, DO NOT MODIFY
-layout: learningpathall
----
-
-In this section, you will view a live camera feed with the ML application running.
-
-## Download the Himax AI web toolkit
-
-The Himax AI web toolkit enables a browser-based graphical user interface (GUI) for the live camera feed.
-
-```bash
-wget https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip
-unzip Himax_AI_web_toolkit.zip
-```
-
-Open the unzipped directory in your file browsing system and double click `index.html`. This will open the GUI within your default browser.
-
-## Connect to the Grove Vision AI
-
-Select `Grove Vision AI(V2)` in the top-right hand corner and press `Connect` button. Follow the instructions to set up the connection.
-
-![Himax web UI](./himax_web_ui.jpg)
-
-The image will run the YOLOv8 on your device. By using the camera to identify things from the [Common Objects in Context (COCO) dataset](https://cocodataset.org/#home), which the model has been trained on, you can put the it to the test. Get some common objects ready and move on to the next section.
-
-## View model results
-
-TODO have running section here?
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md b/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
deleted file mode 100644
index bc84d98418..0000000000
--- a/content/learning-paths/microcontrollers/yolo-on-himax/how-to-5.md
+++ /dev/null
@@ -1,52 +0,0 @@
----
-title: Object detection and additional models
-weight: 6
-
-### FIXED, DO NOT MODIFY
-layout: learningpathall
----
-
-There are other computer vision applications to try. In this section, you will re-flash the module with a different one and check the results.
-
-## Modify the Makefile
-
-Change the directory to the where the Makefile is located. If you cloned the repository to a different location, replace $HOME with the path.
-
-```bash
-cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
-```
-
-The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the Makefile to one of the values in the table.
-
-|APP_TYPE =|Description|
-|---|---|
-|tflm_folov8_od|Object detection|
-|tflm_folov8_pose|Pose detection|
-|tflm_fd_fm|Face detection|
-
-
-## Regenerate the firmware image
-
-Now you can run `make` to re-generate the `.elf` file.
-
-```bash
-make clean
-make
-```
-Use the command from [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/how-to-3/) section to run re-generate the firmware image.
-
-```bash
-python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
-```
-
-The images below are captured images from the models run in the toolkit.
-
-### Objection detection
-![object_detection](./object_detection.jpg)
-
-### Pose estimation
-![Pose estimation](./pose_estimation.jpg)
-
-### Face detection
-![object_detection](./face_detection.jpg)
-
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
new file mode 100644
index 0000000000..ce39140aa4
--- /dev/null
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
@@ -0,0 +1,92 @@
+---
+title: (optional) Run additional models in the web toolkit
+weight: 6
+
+draft: true
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+In this section, you will view a live camera feed with the ML application running.
+
+## Modify the Makefile
+
+Change the directory to the where the Makefile is located. If you cloned the repository to a different location, replace $HOME with the path.
+
+```bash
+cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
+```
+
+The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table.
+
+|APP_TYPE           |Description        |
+|---                |---                |
+|tflm_yolov8_od     |Object detection   |
+|tflm_yolov8_pose   |Pose detection     |
+|tflm_fd_fm         |Face detection     |
+
+
+## Regenerate the firmware image
+
+Now you can run `make` to re-generate the `.elf` file.
+
+```bash
+make clean
+make
+```
+
+Use the commands from [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/flash-and-run/) section to run re-generate the firmware image.
+
+```bash
+cd ../we2_image_gen_local/
+cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
+```
+
+### Linux
+
+```bash
+./we2_local_image_gen project_case1_blp_wlcsp.json
+```
+
+### macOS
+```console
+./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
+```
+
+Finally, use `xmodem` to flash the image.
+
+```bash
+python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+```
+
+Press the reset button when prompted before moving on.
+
+## Download the Himax AI web toolkit
+
+The Himax AI web toolkit enables a browser-based graphical user interface (GUI) for the live camera feed.
+
+```bash
+wget https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/releases/download/v1.1/Himax_AI_web_toolkit.zip
+unzip Himax_AI_web_toolkit.zip
+```
+
+Open the unzipped directory in your file browsing system and double click `index.html`. This will open the GUI within your default browser.
+
+## Connect to the Grove Vision AI
+
+Select `Grove Vision AI(V2)` in the top-right hand corner and press `Connect` button. Follow the instructions to set up the connection. Now you should see a video feed with a bounding box showing identified objects, poses or face detection.
+
+![Himax web UI](./himax_web_ui.jpg)
+
+The images below are captured images from the models run in the toolkit.
+
+### Objection detection
+![object_detection](./object_detection.jpg)
+
+### Pose estimation
+![Pose estimation](./pose_estimation.jpg)
+
+### Face detection
+![object_detection](./face_detection.jpg)
+

From 36f0ee2590ddae02952019ea91c71f43e7f25f93 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Thu, 28 Nov 2024 17:25:31 +0000
Subject: [PATCH 59/87] Editorial update.

---
 .../android_webgpu_dawn/7-Profiling-App-using-Streamline.md   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index 273c619fab..c47fbbaba8 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -10,7 +10,7 @@ layout: learningpathall
 
 Follow these steps to configure Arm Streamline Performance Analyzer to capture Mali GPU related data:
 
-* Confirm your Android device is connected to the development machine.
+* Confirm that your Android device is connected to the development machine.
 * Navigate to the **Start** tab, select **Android (adb)**, select the device, and then select the application to debug.
 * Select **Select Counters**.
 
@@ -38,7 +38,7 @@ Switch to **Mali Timeline** view as shown below:
 
 !["Mali Timeline Streamline" #center](images/Streamline-mali-timeline.png "Mali Timeline Streamline")
 
-You might have to zoom into the data up to the maximum (**500 us**), as you are rendering a very simple 3D object. 
+You might have to zoom into the data up to the maximum (**500 us**), as you are rendering a simple 3D object. 
 
 You can analyze two consecutive frames as shown below:
 

From e68e7dbc651e1f67e72ad4a77bacb6ed597b1ab3 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Thu, 28 Nov 2024 20:06:54 +0100
Subject: [PATCH 60/87] Temporarily disable draft

---
 .../learning-paths/microcontrollers/yolo-on-himax/_index.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 1dcf71ef7a..7666663b81 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -31,9 +31,9 @@ operatingsystems:
     - Linux
     - macOS
 
-draft: true
-cascade:
-    draft: true
+#draft: true
+#cascade:
+#    draft: true
 
 
 ### FIXED, DO NOT MODIFY

From d82be5f61918924f344acb2b748ac1565953c954 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Thu, 28 Nov 2024 20:08:44 +0100
Subject: [PATCH 61/87] Temporarily disable draft

---
 .../microcontrollers/yolo-on-himax/web-toolkit.md               | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
index ce39140aa4..6df3680264 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
@@ -2,7 +2,7 @@
 title: (optional) Run additional models in the web toolkit
 weight: 6
 
-draft: true
+#draft: true
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

From ef95018212220423768e2ea78bbdf42826789da8 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Thu, 28 Nov 2024 17:41:12 +0100
Subject: [PATCH 62/87] Update MLEK LP with Corstone-320 FVP

---
 .../microcontrollers/mlek/_index.md           |   6 +-
 .../microcontrollers/mlek/build.md            | 117 ++++++++++++++----
 .../microcontrollers/mlek/run.md              |  53 +++++---
 3 files changed, 134 insertions(+), 42 deletions(-)

diff --git a/content/learning-paths/microcontrollers/mlek/_index.md b/content/learning-paths/microcontrollers/mlek/_index.md
index 4a0460243f..14729b1732 100644
--- a/content/learning-paths/microcontrollers/mlek/_index.md
+++ b/content/learning-paths/microcontrollers/mlek/_index.md
@@ -1,13 +1,13 @@
 ---
 title: Build and run the Arm Machine Learning Evaluation Kit examples
 
-minutes_to_complete: 30   
+minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for embedded software developers interested in learning about machine learning.
 
-learning_objectives: 
+learning_objectives:
     - Build examples from Machine Learning Evaluation Kit (MLEK)
-    - Run the examples on Corstone-300 FVP or Virtual Hardware
+    - Run the examples on Corstone-320 FVP or Virtual Hardware
 
 prerequisites:
     - Some familiarity with embedded programming
diff --git a/content/learning-paths/microcontrollers/mlek/build.md b/content/learning-paths/microcontrollers/mlek/build.md
index 0872560bfd..98b0e289b8 100644
--- a/content/learning-paths/microcontrollers/mlek/build.md
+++ b/content/learning-paths/microcontrollers/mlek/build.md
@@ -7,69 +7,138 @@ weight: 2 # 1 is first, 2 is second, etc.
 # Do not modify these elements
 layout: "learningpathall"
 ---
-The [Arm ML Evaluation Kit (MLEK)](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ml-embedded-evaluation-kit) provides a number of ready-to-use ML applications. These allow you to investigate the embedded software stack and evaluate performance on the Cortex-M55 and Ethos-U55 processors.
+The [Arm ML Evaluation Kit (MLEK)](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ml-embedded-evaluation-kit) provides a number of ready-to-use ML applications. These allow you to investigate the embedded software stack and evaluate performance on the Cortex-M55 and Ethos-U85 processors.
 
-You can use the MLEK source code to build sample applications and run them on the [Corstone reference systems](https://www.arm.com/products/silicon-ip-subsystems/), for example the [Corstone-300](https://developer.arm.com/Processors/Corstone-300) Fixed Virtual Platform (FVP).
+You can use the MLEK source code to build sample applications and run them on the [Corstone reference systems](https://www.arm.com/products/silicon-ip-subsystems/), for example the [Corstone-320](https://developer.arm.com/Processors/Corstone-320) Fixed Virtual Platform (FVP).
 
 ## Before you begin
 
 You can use your own Ubuntu Linux host machine or use [Arm Virtual Hardware (AVH)](https://www.arm.com/products/development-tools/simulation/virtual-hardware) for this Learning Path.
 
-The Ubuntu version should be 20.04 or 22.04. The `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. You will need a Linux desktop to run the FVP because it opens graphical windows for input and output from the software applications.
+The Ubuntu version should be 20.04 or 22.04. These instructions have been tested on the `x86_64` architecture. You will need a way to interact visually with your machine to run the FVP, because it opens graphical windows for input and output from the software applications.
 
 If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide](/install-guides/avh#corstone) provides setup instructions.
 
-### Compilers
+## Build the example application
 
-The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain).
+### Install the dependencies
 
-Use the install guides to install the compilers on your computer:
-- [Arm Compiler for Embedded](/install-guides/armclang/)
-- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu)
+Run the following commands to install some necessary tools.
 
-Both compilers are pre-installed in Arm Virtual Hardware.
+```bash
+sudo apt update
+sudo apt install unzip python3-venv python3-pip -y
+```
+### Install the compiler
 
-### Corstone-300 FVP {#fvp}
+The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain).
 
-To install the Corstone-300 FVP on your computer refer to the [install guide for Arm Ecosystem FVPs](/install-guides/fm_fvp).
+Install the GNU toolchain (`gcc`).
 
-The Corstone-300 FVP is pre-installed in Arm Virtual Hardware.
+```bash
+wget https://developer.arm.com/-/media/Files/downloads/gnu/13.3.rel1/binrel/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi.tar.xz
 
-## Clone the repository
+tar -xf arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi.tar.xz
 
-1. Install `virtualenv` to create Python virtual environments:
+export PATH=~/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi/bin/:$PATH
 
-```console
-sudo apt update
-sudo apt install python3-venv -y
 ```
 
-2. Clone the ML Evaluation Kit repository, and navigate into the new directory:
+{{% notice Tip %}}
+You can review the installation guides for further details.
 
-```console
+- [Arm Compiler for Embedded](/install-guides/armclang/)
+- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu)
+
+{{% /notice %}}
+
+
+Both compilers are pre-installed in Arm Virtual Hardware.
+
+### Clone the repository
+
+Clone the ML Evaluation Kit repository, and navigate into the new directory:
+
+```bash
 git clone "https://review.mlplatform.org/ml/ethos-u/ml-embedded-evaluation-kit"
 cd ml-embedded-evaluation-kit
 git submodule update --init
 ```
 
-## Build the example applications
+### Run the build sscript
+
+The default build is Ethos-U55  and Corstone-300. The default build for Ethos-U85 is Corstone-320. Use the `npu-config-name` flag to set Ethos-U85.
 
-The default compiler is `gcc`, but `armclang` can also be used.
+The default compiler is `gcc`, but `armclang` can also be used. Number after `ethos-u85-*` is number of MACs, 128-2048 (2^n).
 
 You can select either compiler to build applications. You can also try them both and compare the results.
 
 - Build with Arm GNU Toolchain (`gcc`)
 
 ```
-./build_default.py
+./build_default.py --npu-config-name ethos-u85-256 --toolchain gnu
 ```
 
 - Build with Arm Compiler for Embedded (`armclang`)
 
 ```console
-./build_default.py --toolchain arm
+./build_default.py --npu-config-name ethos-u85-256 --toolchain arm
 ```
 
 The build will take a few minutes.
 
-When the build is complete, you will find the example images (`.axf` files) in the `cmake-build-*/bin` directory. The `cmake-build` directory names are specific to the compiler used and Ethos-U55 configuration.
+When the build is complete, you will find the examples (`.axf` files) in the `cmake-build-*/bin` directory. The `cmake-build` directory names are specific to the compiler used and Ethos-U85 configuration. Verify that the files have been created by observing the output of the `ls` command
+
+```bash
+ls cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/
+```
+
+The next step is to install the FVP and run it with these example audio clips.
+
+
+## Corstone-320 FVP {#fvp}
+
+This section describes installation of the Corstone-320 to run on your local machine. If you are using Arm Virtual Hardware, that comes with the Corstone-300 FVP pre-installed, and you can move on to the next section. You can review Arm's full FVP offer and general installation steps in the [Fast Model and Fixed Virtual Platform](/install-guides/fm_fvp) install guides.
+
+{{% notice Note %}}
+The rest of the steps for the Corstone-320 need to be run in a new terminal window.
+{{% /notice %}}
+
+Open a **new terminal window** and download the Corstone-320 archive.
+
+```bash
+cd $HOME
+wget https://developer.arm.com/-/cdn-downloads/permalink/FVPs-Corstone-IoT/Corstone-320/FVP_Corstone_SSE-320_11.27_25_Linux64.tgz
+```
+
+Unpack it with `tar`, run the setup script and export the binary paths to the `PATH` environment variable.
+
+```bash
+tar -xf FVP_Corstone_SSE-320_11.27_25_Linux64.tgz
+./FVP_Corstone_SSE-320.sh --i-agree-to-the-contained-eula --no-interactive -q
+export PATH=$HOME/FVP_Corstone_SSE-320/models/Linux64_GCC-9.3:$PATH
+```
+
+The FVP requires an additional dependency, `libpython3.9.so.1.0`, which can be installed using a script. Note that this will tinkle with the python installation for the current terminal window, so make sure to open a new one for the next step.
+
+```bash
+source $HOME/FVP_Corstone_SSE-320/scripts/runtime.sh
+```
+
+Verify that the FVP was successfully installed by comparing your output from below command.
+
+```bash
+FVP_Corstone_SSE-320
+```
+
+```output
+telnetterminal0: Listening for serial connection on port 5000
+telnetterminal1: Listening for serial connection on port 5001
+telnetterminal2: Listening for serial connection on port 5002
+telnetterminal5: Listening for serial connection on port 5003
+
+```
+
+
+Now you are ready to test the application with the FVP.
+
diff --git a/content/learning-paths/microcontrollers/mlek/run.md b/content/learning-paths/microcontrollers/mlek/run.md
index 69c9a83399..714002ad57 100644
--- a/content/learning-paths/microcontrollers/mlek/run.md
+++ b/content/learning-paths/microcontrollers/mlek/run.md
@@ -1,6 +1,6 @@
 ---
 # User change
-title: "Run the examples on Corstone-300 FVP"
+title: "Run the examples on the FVP"
 
 weight: 3 # 1 is first, 2 is second, etc.
 
@@ -9,17 +9,37 @@ layout: "learningpathall"
 ---
 ## Run an example
 
-To run an example on the Corstone-300 FVP target, launch the FVP executable with `-a` to specify the software application.
+Now you are ready to combine the FVP installation and the example application. Navigate to the evaluation kit repository.
 
-To run the key word spotting example `ethos-u-kws.axf` compiled with `gcc` use:
+```bash
+cd ml-embedded-evaluation-kit/
+```
+
+To run an example on the Corstone-320 FVP target, launch the FVP executable with `-a` to specify the software application.
+
+To run the key word spotting example `ethos-u-kws.axf` compiled with `gcc` use one of the two options below.
 
-- on your computer with the FVP installed
+## Option 1: On your computer with the FVP installed
+
+Run the FVP.
 
 ```console
-FVP_Corstone_SSE-300_Ethos-U55 -a cmake-build-mps3-sse-300-ethos-u55-128-gnu/bin/ethos-u-kws.axf
+FVP_Corstone_SSE-320                                    \
+    -C mps4_board.subsystem.ethosu.num_macs=256         \
+    -C mps4_board.visualisation.disable-visualisation=1 \
+    -C vis_hdlcd.disable_visualisation=1                \
+    -a cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/ethos-u-kws.axf
 ```
 
-- on Arm Virtual Hardware
+{{% notice Note %}}
+The number of NPU MACs specified in the build MUST match the number specified in the FVP. Else an error similar to the below will be emitted.
+
+```
+E: NPU config mismatch. npu.macs_per_cc=E: NPU config mismatch..
+```
+{{% /notice %}}
+
+## Option 2: On Arm Virtual Hardware
 
 ```console
 VHT_Corstone_SSE-300_Ethos-U55 -a cmake-build-mps3-sse-300-ethos-u55-128-gnu/bin/ethos-u-kws.axf
@@ -27,17 +47,18 @@ VHT_Corstone_SSE-300_Ethos-U55 -a cmake-build-mps3-sse-300-ethos-u55-128-gnu/bin
 When the example is running, a telnet instance will open allowing you to interact with the example.
 
 {{% notice Note %}}
-It may take some time to initialize the terminal, please be patient. 
+It may take some time to initialize the terminal, please be patient.
 
 If you see warnings regarding loading the image, these can likely be ignored.
 {{% /notice %}}
 
+## Interact with the application
 
 Use the menu to control the application. For the key word spotting application enter 1 to classify the next audio clip.
 
 ![terminal #center](term.png)
 
-The results of the classification will appear in the visualization window of the FVP. 
+The results of the classification will appear in the visualization window of the FVP.
 
 The display shows a 98% chance of the audio clips sound was down.
 
@@ -56,23 +77,23 @@ You can specify additional parameters to configure certain aspects of the simula
 List the available parameters by running the FVP executable with the `--list-params` option, for example:
 
 ```console
-FVP_Corstone_SSE-300_Ethos-U55 --list-params > parameters.txt
+FVP_Corstone_SSE-320 --list-params > parameters.txt
 ```
 
 {{% notice Note %}}
 If you are running with Arm Virtual Hardware substitute `VHT_Corstone_SSE-300_Ethos-U55` as the executable name.
 {{% /notice %}}
 
-Open the file `parameters.txt` to see all of the possible parameters and the default values. 
+Open the file `parameters.txt` to see all of the possible parameters and the default values.
 
 ### Set parameters
 
-Individual parameters can be set with the `-C` command option. 
+Individual parameters can be set with the `-C` command option.
 
 For example, to put the Ethos-U component into fast execution mode:
 
 ```console
-FVP_Corstone_SSE-300_Ethos-U55 -a cmake-build-mps3-sse-300-ethos-u55-128-gnu/bin/ethos-u-kws.axf -C ethosu.extra_args="--fast"
+FVP_Corstone_SSE-320 -a cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/ethos-u-kws.axf -C mps4_board.subsystem.ethosu.extra_args="--fast"
 ```
 {{% notice Note %}}
 Do not use fast execution mode whilst benchmarking performance.
@@ -83,14 +104,16 @@ To set multiple parameters it may be easier to list them in a text file (without
 For example, use a text editor to create a file named `options.txt` with the contents:
 
 ```console
-mps3_board.visualisation.disable-visualisation=1
-ethosu.extra_args="--fast"
+mps4_board.visualisation.disable-visualisation=1
+mps4_board.subsystem.ethosu.extra_args="--fast"
 ```
 
 Run the FVP with the `-f` option and the `options.txt` file:
 
 ```console
-FVP_Corstone_SSE-300_Ethos-U55 -a cmake-build-mps3-sse-300-ethos-u55-128-gnu/bin/ethos-u-kws.axf -f options.txt
+FVP_Corstone_SSE-320 -a cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/ethos-u-kws.axf -f options.txt
 ```
 
 Full instructions are provided in the evaluation kit [documentation](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ml-embedded-evaluation-kit/+/HEAD/docs/quick_start.md).
+
+You have now run an example application on an Arm Fixed Virtual Platform.
\ No newline at end of file

From a87b4e62b781a8775516201f7493fbde343cafd9 Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Fri, 29 Nov 2024 13:26:00 +0100
Subject: [PATCH 63/87] Update web-toolkit.md

---
 .../yolo-on-himax/web-toolkit.md              | 22 +++++++++++++------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
index 6df3680264..0790b4ff35 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
@@ -18,13 +18,18 @@ Change the directory to the where the Makefile is located. If you cloned the rep
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
 ```
 
-The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table.
+The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table. Then, to the xmodem argument, pass the `--model` argument.
+
+|APP_TYPE           |Description        | --model argument |
+|---                |---                |---
+|tflm_yolov8_od     |Object detection   | model_zoo\tflm_yolov8_od\yolov8n_od_192_delete_transpose_0xB7B000.tflite 0xB7B000 0x00000 |
+|tflm_yolov8_pose   |Pose detection     | model_zoo\tflm_yolov8_pose\yolov8n_pose_256_vela_3_9_0x3BB000.tflite 0x3BB000 0x00000 |
+|tflm_fd_fm         |Face detection     | model_zoo\tflm_fd_fm\0_fd_0x200000.tflite 0x200000 0x00000 model_zoo\tflm_fd_fm\1_fm_0x280000.tflite 0x280000 0x00000 model_zoo\tflm_fd_fm\2_il_0x32A000.tflite 0x32A000 0x00000 |
+
+{{% notice Note %}}
+For `tflm_fd_fm`, you need to pass all three models as separate `--model` arguments.
+{{% /notice %}}
 
-|APP_TYPE           |Description        |
-|---                |---                |
-|tflm_yolov8_od     |Object detection   |
-|tflm_yolov8_pose   |Pose detection     |
-|tflm_fd_fm         |Face detection     |
 
 
 ## Regenerate the firmware image
@@ -57,7 +62,10 @@ cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_
 Finally, use `xmodem` to flash the image.
 
 ```bash
-python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+python xmodem\xmodem_send.py --port=<COM port> \
+--baudrate=921600 --protocol=xmodem \
+--file=we2_image_gen_local\output_case1_sec_wlcsp\output.img \
+--model=<model argument>
 ```
 
 Press the reset button when prompted before moving on.

From f898b5f4c0fba7c732afc8b78df10e92007d1b14 Mon Sep 17 00:00:00 2001
From: GitHub Actions Stats Bot <>
Date: Mon, 2 Dec 2024 01:29:06 +0000
Subject: [PATCH 64/87] automatic update of stats files

---
 data/stats_current_test_info.yml |  2 +-
 data/stats_weekly_data.yml       | 84 ++++++++++++++++++++++++++++++++
 2 files changed, 85 insertions(+), 1 deletion(-)

diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml
index 1eca8f7819..3947475798 100644
--- a/data/stats_current_test_info.yml
+++ b/data/stats_current_test_info.yml
@@ -1,5 +1,5 @@
 summary:
-  content_total: 303
+  content_total: 305
   content_with_all_tests_passing: 32
   content_with_tests_enabled: 34
 sw_categories:
diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml
index 280ec08538..e9e8de919a 100644
--- a/data/stats_weekly_data.yml
+++ b/data/stats_weekly_data.yml
@@ -4002,3 +4002,87 @@
     avg_close_time_hrs: 0
     num_issues: 13
     percent_closed_vs_total: 0.0
+- a_date: '2024-12-02'
+  content:
+    cross-platform: 25
+    embedded-systems: 19
+    install-guides: 90
+    laptops-and-desktops: 33
+    microcontrollers: 25
+    servers-and-cloud-computing: 88
+    smartphones-and-mobile: 25
+    total: 305
+  contributions:
+    external: 43
+    internal: 355
+  github_engagement:
+    num_forks: 30
+    num_prs: 14
+  individual_authors:
+    alaaeddine-chakroun: 1
+    alexandros-lamprineas: 1
+    annie-tallund: 1
+    arm: 3
+    arnaud-de-grandmaison: 1
+    basma-el-gaabouri: 1
+    ben-clark: 1
+    bolt-liu: 2
+    brenda-strech: 1
+    chaodong-gong,-alex-su,-kieran-hejmadi: 1
+    chen-zhang: 1
+    christopher-seidl: 7
+    cyril-rohr: 1
+    daniel-gubay: 1
+    daniel-nguyen: 1
+    david-spickett: 2
+    dawid-borycki: 30
+    diego-russo: 1
+    diego-russo-and-leandro-nunes: 1
+    elham-harirpoush: 2
+    florent-lebeau: 5
+    "fr\xE9d\xE9ric--lefred--descamps": 2
+    gabriel-peterson: 5
+    gayathri-narayana-yegna-narayanan: 1
+    graham-woodward: 1
+    iago-calvo-lista,-arm: 1
+    james-whitaker,-arm: 1
+    jason-andrews: 90
+    joe-stech: 1
+    johanna-skinnider: 2
+    jonathan-davies: 2
+    jose-emilio-munoz-lopez,-arm: 1
+    julie-gaskin: 4
+    julio-suarez: 5
+    kasper-mecklenburg: 1
+    koki-mitsunami: 2
+    konstantinos-margaritis: 7
+    kristof-beyls: 1
+    liliya-wu: 1
+    mathias-brossard: 1
+    michael-hall: 5
+    nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1
+    odin-shen: 1
+    owen-wu,-arm: 2
+    pareena-verma: 35
+    pareena-verma,-annie-tallund: 1
+    pareena-verma,-jason-andrews,-and-zach-lasiuk: 1
+    pareena-verma,-joe-stech,-adnan-alsinan: 1
+    pranay-bakre: 4
+    przemyslaw-wirkus: 1
+    rin-dobrescu: 1
+    roberto-lopez-mendez: 2
+    ronan-synnott: 45
+    thirdai: 1
+    tianyu-li: 1
+    tom-pilar: 1
+    uma-ramalingam: 1
+    varun-chari,-pareena-verma: 1
+    visualsilicon: 1
+    ying-yu: 1
+    ying-yu,-arm: 1
+    zach-lasiuk: 1
+    zhengjun-xing: 2
+  issues:
+    avg_close_time_hrs: 0
+    num_issues: 18
+    percent_closed_vs_total: 0.0

From 3867a5c4706e6825a0da5ed6c24964318a102dc8 Mon Sep 17 00:00:00 2001
From: GitHub Actions Stats Bot <>
Date: Mon, 2 Dec 2024 02:38:46 +0000
Subject: [PATCH 65/87] automatic update of stats files

---
 data/stats_current_test_info.yml |  2 +-
 data/stats_weekly_data.yml       | 84 ++++++++++++++++++++++++++++++++
 2 files changed, 85 insertions(+), 1 deletion(-)

diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml
index 1eca8f7819..3947475798 100644
--- a/data/stats_current_test_info.yml
+++ b/data/stats_current_test_info.yml
@@ -1,5 +1,5 @@
 summary:
-  content_total: 303
+  content_total: 305
   content_with_all_tests_passing: 32
   content_with_tests_enabled: 34
 sw_categories:
diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml
index 280ec08538..e9e8de919a 100644
--- a/data/stats_weekly_data.yml
+++ b/data/stats_weekly_data.yml
@@ -4002,3 +4002,87 @@
     avg_close_time_hrs: 0
     num_issues: 13
     percent_closed_vs_total: 0.0
+- a_date: '2024-12-02'
+  content:
+    cross-platform: 25
+    embedded-systems: 19
+    install-guides: 90
+    laptops-and-desktops: 33
+    microcontrollers: 25
+    servers-and-cloud-computing: 88
+    smartphones-and-mobile: 25
+    total: 305
+  contributions:
+    external: 43
+    internal: 355
+  github_engagement:
+    num_forks: 30
+    num_prs: 14
+  individual_authors:
+    alaaeddine-chakroun: 1
+    alexandros-lamprineas: 1
+    annie-tallund: 1
+    arm: 3
+    arnaud-de-grandmaison: 1
+    basma-el-gaabouri: 1
+    ben-clark: 1
+    bolt-liu: 2
+    brenda-strech: 1
+    chaodong-gong,-alex-su,-kieran-hejmadi: 1
+    chen-zhang: 1
+    christopher-seidl: 7
+    cyril-rohr: 1
+    daniel-gubay: 1
+    daniel-nguyen: 1
+    david-spickett: 2
+    dawid-borycki: 30
+    diego-russo: 1
+    diego-russo-and-leandro-nunes: 1
+    elham-harirpoush: 2
+    florent-lebeau: 5
+    "fr\xE9d\xE9ric--lefred--descamps": 2
+    gabriel-peterson: 5
+    gayathri-narayana-yegna-narayanan: 1
+    graham-woodward: 1
+    iago-calvo-lista,-arm: 1
+    james-whitaker,-arm: 1
+    jason-andrews: 90
+    joe-stech: 1
+    johanna-skinnider: 2
+    jonathan-davies: 2
+    jose-emilio-munoz-lopez,-arm: 1
+    julie-gaskin: 4
+    julio-suarez: 5
+    kasper-mecklenburg: 1
+    koki-mitsunami: 2
+    konstantinos-margaritis: 7
+    kristof-beyls: 1
+    liliya-wu: 1
+    mathias-brossard: 1
+    michael-hall: 5
+    nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1
+    odin-shen: 1
+    owen-wu,-arm: 2
+    pareena-verma: 35
+    pareena-verma,-annie-tallund: 1
+    pareena-verma,-jason-andrews,-and-zach-lasiuk: 1
+    pareena-verma,-joe-stech,-adnan-alsinan: 1
+    pranay-bakre: 4
+    przemyslaw-wirkus: 1
+    rin-dobrescu: 1
+    roberto-lopez-mendez: 2
+    ronan-synnott: 45
+    thirdai: 1
+    tianyu-li: 1
+    tom-pilar: 1
+    uma-ramalingam: 1
+    varun-chari,-pareena-verma: 1
+    visualsilicon: 1
+    ying-yu: 1
+    ying-yu,-arm: 1
+    zach-lasiuk: 1
+    zhengjun-xing: 2
+  issues:
+    avg_close_time_hrs: 0
+    num_issues: 18
+    percent_closed_vs_total: 0.0

From ec1e112db841f2f5ea4224de8c8b859b7fdd1bdb Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 2 Dec 2024 07:25:07 +0000
Subject: [PATCH 66/87] Editorial.

---
 .../5-render-a-simple-3D-object-part-1.md     | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
index f807a40b92..322abd6106 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
@@ -8,40 +8,40 @@ layout: learningpathall
 
 ## Command queue
 
-Graphic applications have to deal with 2 processors, the CPU and the GPU. 
+Graphic applications must operate with two processors, the CPU and the GPU. 
 
-These 2 processors run on different timelines. For optimal performance, commands intended for the GPU are batched and sent through a command queue. The GPU consumes this queue whenever it is ready, and this way processors minimize the time spent idling for their sibling to respond. 
+These two processors run on different timelines. For optimal performance, commands intended for the GPU are batched and sent through a command queue. The GPU consumes this queue whenever it is ready, and in this way processors minimize the time spent idling for their sibling to respond. 
 
 A WebGPU device has a single queue, which is used to send both commands and data. You can get it with `wgpuDeviceGetQueue()`.
 
-WebGPU offers 3 different ways to submit work to this queue:
+WebGPU offers three different ways to submit work to this queue:
 
-* wgpuQueueSubmit
-* wgpuQueueWriteBuffer
-* wgpuQueueWriteTexture
+* wgpuQueueSubmit.
+* wgpuQueueWriteBuffer.
+* wgpuQueueWriteTexture.
 
 {{% notice Note %}}
-Other graphics API allow you to build multiple queues per device, and future version of WebGPU might as well. 
+Other graphics API allow you to build multiple queues per device. 
 
-If you want to learn more, refer to the [Command Queue](https://eliemichel.github.io/LearnWebGPU/getting-started/the-command-queue.html)
+If you would like to learn more, refer to the [Command Queue](https://eliemichel.github.io/LearnWebGPU/getting-started/the-command-queue.html).
 {{% /notice %}}
 
 ## Getting started to render a 3D object
 
-WebGPU is a very simple system. All it does is run 3 types of functions on the GPU: Vertex Shaders, Fragment Shaders, and Compute Shaders.
+WebGPU is a very simple system. It runs three types of functions on the GPU: 
 
-* A Vertex Shader computes vertices. The shader returns vertex positions.
-* A Fragment Shader computes colors, they indirectly write data to textures. That data does not have to be colors.
-* A Compute Shader is more generic. It’s effectively a function you call and say “execute this function N times”
+* A Vertex Shader that computes vertices. The shader returns vertex positions.
+* A Fragment Shader that computes colors. The shader indirectly writes data to textures. That data does not have to be limited to colors.
+* A Compute Shader that is more generic. It is effectively a function that you can call and request to execute as many times as you require.
 
-Here is a simplified diagram of WebGPU setup to draw triangles by using a vertex shader and a fragment shader:
+Here is a simplified diagram of a WebGPU setup to draw triangles by using a vertex shader and a fragment shader:
 
 !["Triangle using WebGPU" #center](images/webgpu-draw-high-level.svg "Triangle using WebGPU")
 
 The main things to notice in the above image are:
 
-* There is a **Pipeline**. It contains the vertex shader and fragment shader the GPU will run. You could also have a pipeline with a compute shader.
-* The shaders reference resources (buffers, textures, samplers) indirectly through **Bind Groups**.
+* There is a **Pipeline** that contains the vertex shader and fragment shader the GPU runs. Alternatively, you might also have a pipeline with a compute shader.
+* The shaders reference resources such as buffers, textures, and samplers, indirectly through **Bind Groups**.
 * The pipeline defines attributes that reference buffers indirectly through the internal state.
 * Attributes pull data out of buffers and feed the data into the vertex shader.
 * The vertex shader may feed data into the fragment shader.

From b783561e0ad2d3a8b8292ed8e1840e9c71a5261c Mon Sep 17 00:00:00 2001
From: Annie Tallund <annie.tallund@arm.com>
Date: Mon, 2 Dec 2024 12:22:04 +0100
Subject: [PATCH 67/87] Update web-toolkit.md

- Additional use-cases verified
- Misc minor updates
- Remove _review.md
---
 .../microcontrollers/yolo-on-himax/_index.md  |  6 +--
 .../microcontrollers/yolo-on-himax/_review.md | 38 -------------------
 .../yolo-on-himax/build-firmware.md           | 18 ++++-----
 .../microcontrollers/yolo-on-himax/dev-env.md | 32 +++++++---------
 .../yolo-on-himax/flash-and-run.md            | 25 ++++++------
 .../yolo-on-himax/web-toolkit.md              | 30 ++++++---------
 6 files changed, 51 insertions(+), 98 deletions(-)
 delete mode 100644 content/learning-paths/microcontrollers/yolo-on-himax/_review.md

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 7666663b81..1dcf71ef7a 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -31,9 +31,9 @@ operatingsystems:
     - Linux
     - macOS
 
-#draft: true
-#cascade:
-#    draft: true
+draft: true
+cascade:
+    draft: true
 
 
 ### FIXED, DO NOT MODIFY
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md b/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
deleted file mode 100644
index 98489df86d..0000000000
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_review.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-review:
-    - questions:
-        question: >
-            The Grove Vision AI V2 Module can run YOLOv8 model in real time
-        answers:
-            - True
-            - False
-        correct_answer: 1
-        explanation: >
-            The Grove Vision AI V2 Module can run object detection in real time thanks to it's ML accelerated capabilites.
-        question: >
-            Which of the options is the YOLO model unable to run?
-        answers:
-            - Pose detection
-            - Object detection
-            - Speech-to-text transcription
-        correct_answer: 3
-        explanation: >
-            The YOLO model is a computer vision model, meaning it runs based on images as input.
-        question: >
-            What Arm IP on the Seeed Grove Vision AI Module V2 enables you to run ML workloads efficiently?
-        answers:
-            - Ethos-U55
-            - Cortex-A72
-            - Cortex-X4
-        correct_answer: 1
-        explanation: >
-            When paired with the low-power Cortex-M55 processor, the Ethos-U55 provides an uplift in ML performance
-
-
-# ================================================================================
-#       FIXED, DO NOT MODIFY
-# ================================================================================
-title: "Review"                 # Always the same title
-weight: 20                      # Set to always be larger than the content in this path
-layout: "learningpathall"       # All files under learning paths have this same wrapper
----
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
index d2f5604b99..ef89acd6d3 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
@@ -34,24 +34,24 @@ make
 
 ## Generate the firmware image
 
-Copy the `.elf` file to the `input_case1_secboot` directory.
+The examples repository contains scripts to generate the image file. Copy the `.elf` file to the `input_case1_secboot` directory.
 
 ```bash
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ```
-The examples repository contains scripts to generate the image. Run the script corresponding to the OS of your host machine.
 
-### Linux
+Run the script corresponding to the OS of your host machine. This will create a file named `output.img` in the `output_case1_sec_wlcsp` directory.
 
-```bash
-./we2_local_image_gen project_case1_blp_wlcsp.json
-```
 
-### macOS
-```console
+{{< tabpane code=true >}}
+  {{< tab header="Linux" language="shell">}}
+./we2_local_image_gen project_case1_blp_wlcsp.json
+  {{< /tab >}}
+  {{< tab header="MacOS" language="shell">}}
 ./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
-```
+  {{< /tab >}}
+{{< /tabpane >}}
 
 Your terminal output should end with the following.
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md b/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
index cd5ceb2f83..450297b752 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
@@ -67,18 +67,15 @@ git version 2.39.3
 
 Install the make build tool, which is used to build the firmware in the next section.
 
-### Linux
-
-```bash
+{{< tabpane code=true >}}
+  {{< tab header="Linux" language="shell">}}
 sudo apt update
 sudo apt install make -y
-```
-
-### macOS
-
-```console
+  {{< /tab >}}
+  {{< tab header="MacOS" language="shell">}}
 brew install make
-```
+  {{< /tab >}}
+{{< /tabpane >}}
 
 Successful installation of make will show the following when the `make --version` command is run.
 
@@ -96,26 +93,25 @@ To run this learning path on macOS, you need to verify that your installation is
 {{% /notice %}}
 ## Install Arm GNU toolchain
 
-### Linux
-
-The toolchain is used to cross-compile from the host architecture (x86) to the embedded device architecture (AArch64).
+The toolchain is used to compile code from the host to the embedded device architecture.
 
-```bash
+{{< tabpane code=true >}}
+  {{< tab header="Linux" language="shell">}}
 cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 export PATH="$HOME/arm-gnu-toolchain-13.2.Rel1-x86_64-arm-none-eabi/bin/:$PATH"
-```
-### macOS
-```console
+  {{< /tab >}}
+  {{< tab header="MacOS" language="shell">}}
 cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.3.rel1/binrel/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
 export PATH="$HOME/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi/bin/:$PATH"
-```
+  {{< /tab >}}
+{{< /tabpane >}}
 
 {{% notice %}}
-You can add the above command to the `.bashrc` file. This was, the Arm GNU toolchain is configured from new terminal sessions as well.
+You can add the `export` command to the `.bashrc` file. This was, the Arm GNU toolchain is configured from new terminal sessions as well.
 {{% /notice %}}
 
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
index 71e16134fd..78d03dcf5b 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
@@ -19,7 +19,7 @@ pip install -r xmodem/requirements.txt
 
 ## Connect the module
 
-Insert the Flexible printed circuit (FPC) into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
+To prepare for the next steps, it's time to get the board set up. Insert the Flexible printed circuit (FPC) into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
 
 ![unlatched](./unlatched.jpg)
 
@@ -27,7 +27,7 @@ Then, slide the FPC connector in with the metal pins facing down and close the d
 
 ![latched](./latched.jpg)
 
-Then connect the Groove Vision AI V2 Module to your computer via the USB-C cable.
+Now you can connect the Groove Vision AI V2 Module to your computer via the USB-C cable.
 
 {{% notice Note %}}
 The development board may have two USB-C connectors. If you are running into issues connecting the board in the next step, make sure you are using the right one.
@@ -37,17 +37,16 @@ The development board may have two USB-C connectors. If you are running into iss
 
 You'll need to provide the communication port (COM) which the board is connected to in order to flash the image. There are commands to list all COMs available on your machine. Once your board is connected through USB, it'll show up in this list. The COM identifier will start with **tty**, which may help you determine which one it is. You can run the command before and after plugging in the board if you are unsure.
 
-### Linux
 
-```bash
+{{< tabpane code=true >}}
+  {{< tab header="Linux" language="shell">}}
 sudo grep -i 'tty' /var/log/dmesg
-```
-
-### MacOS
-
-```console
+  {{< /tab >}}
+  {{< tab header="MacOS" language="shell">}}
 ls /dev/tty.*
-```
+  {{< /tab >}}
+{{< /tabpane >}}
+
 
 {{% notice Note %}}
 If the port seems unavailable, try changing the permissions temporarily using the `chmod` command. Be sure to reset them afterwards, as this may pose a computer security vulnerability.
@@ -64,11 +63,13 @@ The full path to the port is needed in the next step, so be sure to note it down
 Run the python script below to flash the firmware.
 
 ```bash
-python xmodem\xmodem_send.py --port=<COM port> --baudrate=921600 --protocol=xmodem --file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
+python xmodem\xmodem_send.py --port=<COM port> \
+--baudrate=921600 --protocol=xmodem \
+--file=we2_image_gen_local\output_case1_sec_wlcsp\output.img
 ```
 
 {{% notice Note %}}
-When you run other example models demonstrated in the later section [Object detection and additional models](/learning-paths/microcontrollers/yolo-on-himax/how-to-5/), you need to adapt this command with the right image file.
+When you run other example models demonstrated in the later section [Run additional models in the web toolkit](/learning-paths/microcontrollers/yolo-on-himax/web-toolkit/), you need to adapt this command with `--model` argument.
 {{% /notice %}}
 
 After the firmware image burning is completed, the message `Do you want to end file transmission and reboot system? (y)` is displayed. Press the reset button indicated in the image below.
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
index 0790b4ff35..f3ec67106b 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
@@ -1,14 +1,12 @@
 ---
-title: (optional) Run additional models in the web toolkit
+title: Run additional models in the web toolkit
 weight: 6
 
-#draft: true
-
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-In this section, you will view a live camera feed with the ML application running.
+In this section, you will view a live camera feed with a computer vision application running.
 
 ## Modify the Makefile
 
@@ -18,12 +16,11 @@ Change the directory to the where the Makefile is located. If you cloned the rep
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
 ```
 
-The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table. Then, to the xmodem argument, pass the `--model` argument.
+The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table. Then pass the `--model` argument to the python `xmodem` command.
 
-|APP_TYPE           |Description        | --model argument |
+|APP_TYPE           |Description        | Model argument |
 |---                |---                |---
 |tflm_yolov8_od     |Object detection   | model_zoo\tflm_yolov8_od\yolov8n_od_192_delete_transpose_0xB7B000.tflite 0xB7B000 0x00000 |
-|tflm_yolov8_pose   |Pose detection     | model_zoo\tflm_yolov8_pose\yolov8n_pose_256_vela_3_9_0x3BB000.tflite 0x3BB000 0x00000 |
 |tflm_fd_fm         |Face detection     | model_zoo\tflm_fd_fm\0_fd_0x200000.tflite 0x200000 0x00000 model_zoo\tflm_fd_fm\1_fm_0x280000.tflite 0x280000 0x00000 model_zoo\tflm_fd_fm\2_il_0x32A000.tflite 0x32A000 0x00000 |
 
 {{% notice Note %}}
@@ -47,17 +44,17 @@ Use the commands from [Flash firmware onto the microcontroller](/learning-paths/
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ```
+Run the script corresponding to the OS of your host machine.
 
-### Linux
-
-```bash
+{{< tabpane code=true >}}
+  {{< tab header="Linux" language="shell">}}
 ./we2_local_image_gen project_case1_blp_wlcsp.json
-```
-
-### macOS
-```console
+  {{< /tab >}}
+  {{< tab header="MacOS" language="shell">}}
 ./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
-```
+  {{< /tab >}}
+{{< /tabpane >}}
+
 
 Finally, use `xmodem` to flash the image.
 
@@ -92,9 +89,6 @@ The images below are captured images from the models run in the toolkit.
 ### Objection detection
 ![object_detection](./object_detection.jpg)
 
-### Pose estimation
-![Pose estimation](./pose_estimation.jpg)
-
 ### Face detection
 ![object_detection](./face_detection.jpg)
 

From d32227973621232c681a2f1227aec1502c3627da Mon Sep 17 00:00:00 2001
From: Przemyslaw Wirkus <Przemyslaw.Wirkus@arm.com>
Date: Mon, 2 Dec 2024 11:33:37 +0000
Subject: [PATCH 68/87] windowsperf: update WindowsPerf + WPA plugin docs

---
 .../windowsperf_wpa_plugin.md                    | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
index 2f1f1a04e7..d2bad14c28 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
@@ -26,7 +26,7 @@ You can save a `.json` output from WindowsPerf by using the `--output` command f
     To create a file named `example.json`, run the following command:
 
     ```console
-    wperf stat -e ld_spec --output example.json
+    wperf stat -e ld_spec --timeout 5 --json --output example.json
     ```
 
 2. Open Windows Performance Analyzer, and see the following window:
@@ -54,9 +54,11 @@ You can use WPA to visualize PMU events in the recorded data.
 To try the timeline feature, run the command:
 
 ```command 
-wperf stat -m dcache -c 0,1,2,3,4,5,6,7 -t -i 0 -n 50 --json
+wperf stat -m dcache -c 0,1,2,3,4,5,6,7 -t -i 0 -n 10 --timeout 2 --json --output example2.json
 ```
 
+Note: above command will run for ~20 seconds.
+
 Open the generated output (`.json` file) in WPA to see the graph:
 
 ![timeline-by-core #center](figures/timeline-by-core.png)
@@ -72,9 +74,11 @@ To see all the generated graphs you can expand the `Counting timeline` section i
 Run another `wperf` command with different options:
 
 ```console 
-wperf stat -t -i 0 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --json
+wperf stat -t -i 0 -n 10 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --timeout 2 --json --output --example3.json
 ```
 
+Note: above command will run for ~20 seconds.
+
 The graph after opening the `.json` file is shown below:
 
 ![timeline-events-by-key #center](figures/timeline-events-by-key.png)
@@ -88,9 +92,11 @@ The WPA Plugin also provides visualization of [Arm telemetry metrics](https://de
 To visualize telemetry, run the following command:
 
 ```console 
-wperf stat -t -i 0 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --json
+wperf stat -t -i 0 -n 10 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --timeout 2 --json --output example4.json
 ```
 
+Note: above command will run for ~20 seconds.
+
 You can also see the telemetry timeline graphs under the graph explorer level in WPA. 
 
 These graphs are generated dynamically so only the relevant metrics for the given `.json` output file are visible.
@@ -101,4 +107,4 @@ Once expanded, a more in-depth view is visible under the Analysis tab of WPA.
 
 ![telemetry-table #center](figures/telemetry-table.png)
 
-You now have a basic understanding of how to use `wperf` generated data in the Windows Performance Analyzer.
\ No newline at end of file
+You now have a basic understanding of how to use `wperf` generated data in the Windows Performance Analyzer.

From c2cbe580177728c6c4e33c6765986d2bf615a73f Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Mon, 2 Dec 2024 12:29:32 +0000
Subject: [PATCH 69/87] Editorial.

---
 .../1-webgpu-fundamentals.md                  | 10 +++++--
 .../5-render-a-simple-3D-object-part-1.md     |  8 +++---
 .../6-render-a-simple-3D-object-part-2.md     | 26 +++++++++----------
 .../android_webgpu_dawn/_index.md             |  2 +-
 4 files changed, 26 insertions(+), 20 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
index d337d0296d..735e056a7b 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
@@ -10,9 +10,15 @@ layout: learningpathall
 
 WebGPU is the successor to WebGL, a well-adopted modern API standard for interfacing with GPUs. 
 
-WebGPU offers improved compatibility with modern GPUs, support for general-purpose GPU computations, faster operations, and access to more advanced GPU features. It is designed to provide a unified access to GPUs, that is both GPU vendor-agnostic and OS-agnostic.
+WebGPU offers the following benefits:
 
-WebGPU is a Render Hardware Interface built on top of various backend APIs such as Vulkan, DirectX, and Metal - though this does depend on the operating system. 
+* Improved compatibility with modern GPUs.
+* Support for general-purpose GPU computations.
+* Fast operations.
+* Access to advanced GPU features. 
+* Unified access to GPUs, which is both GPU vendor-agnostic and OS-agnostic.
+
+WebGPU is a render hardware interface built on top of various backend APIs such as Vulkan, DirectX, and Metal, though this is OS-dependent. 
 
 WebGPU is available through web browsers using the webgpu.h header file. 
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
index 322abd6106..24416a80ff 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
@@ -28,7 +28,7 @@ If you would like to learn more, refer to the [Command Queue](https://eliemichel
 
 ## Getting started to render a 3D object
 
-WebGPU is a very simple system. It runs three types of functions on the GPU: 
+WebGPU is a simple system. It runs three types of functions on the GPU: 
 
 * A Vertex Shader that computes vertices. The shader returns vertex positions.
 * A Fragment Shader that computes colors. The shader indirectly writes data to textures. That data does not have to be limited to colors.
@@ -36,7 +36,7 @@ WebGPU is a very simple system. It runs three types of functions on the GPU:
 
 Here is a simplified diagram of a WebGPU setup to draw triangles by using a vertex shader and a fragment shader:
 
-!["Triangle using WebGPU" #center](images/webgpu-draw-high-level.svg "Triangle using WebGPU")
+!["Triangle using WebGPU" #center](images/webgpu-draw-high-level.svg "Figure 8: Triangle using WebGPU")
 
 The main things to notice in the above image are:
 
@@ -61,9 +61,9 @@ In order to achieve high performance real-time 3D rendering, the GPU processes s
 
 To do so, WebGPU provides a Render Pipeline object. The figure below illustrates the sequence of data processing stages executed by the render pipeline.
 
-!["Render Pipeline" #center](images/render-pipeline.svg "Render Pipeline")
+!["Render Pipeline" #center](images/render-pipeline.svg "Figure 9: Render Pipeline")
 
-The Render Pipeline has 2 main types of stages, **fixed-function** and **programmable**.
+The Render Pipeline has two main types of stages, **fixed-function** and **programmable**.
 
 ### Fixed Functions stages
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
index 6378bd1a67..04c8f30cf7 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
@@ -8,29 +8,29 @@ layout: learningpathall
 
 ## 3D meshes
 
-Once a Render Pipeline is created, you can use WebGPU APIs to create and render a 3D mesh. This is very similar to other graphics APIs. 
+Once a Render Pipeline is created, you can use WebGPU APIs to create and render a 3D mesh. This is similar to other graphics APIs. 
 
 The steps are listed below:
 
-* Create the Vertex Buffer(s)
-* Create the Index Buffer(s)
-* Create the Uniform Buffer(s)
-* Create a Depth Buffer (Z-Buffer algorithm)
-* Create the Depth Texture and TextureView
-* Create a Depth Stencil
-* Create the Transformation and Projection matrices
+* Create the Vertex Buffer(s).
+* Create the Index Buffer(s).
+* Create the Uniform Buffer(s).
+* Create a Depth Buffer (Z-Buffer algorithm).
+* Create the Depth Texture and TextureView.
+* Create a Depth Stencil.
+* Create the Transformation and Projection matrices.
 
-All these steps are common in graphics programming and WebGPU offers capability to perform all the operations. 
+All these steps are common in graphics programming, and WebGPU allows you to perform these same operations. 
 
-It is recommended to go through individual chapters in the [3D rendering](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/index.html) section to learn more.
+It is a good idea to read the individual chapters in the [3D rendering](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/index.html) section to learn more.
 
 ### Loading 3D objects
 
-In this project you can use OBJ files to define 3D meshes. 
+In this Learning Path you can use OBJ files to define 3D meshes. 
 
-Instead of manually parsing OBJ files, use the [TinyOBJLoader](https://github.com/tinyobjloader/tinyobjloader) library. 
+Instead of manually parsing OBJ files, you can use the [TinyOBJLoader](https://github.com/tinyobjloader/tinyobjloader) library. 
 
-The file format is not complex, but parsing files is not the goal of this Learning Path. 
+The file format is not complex, but parsing files is out of the scope of this Learning Path. 
 
 You can use open-source software such as Blender to create your own 3D objects.
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index d6c17d25a6..3a05c5e9b9 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -3,7 +3,7 @@ title: Build and profile a simple WebGPU Android Application
 cascade:
 minutes_to_complete: 90
 
-who_is_this_for: This is an introductory topic for developers who are building GPU-based Android applications and are interested in trying WebGPU. 
+who_is_this_for: This is an introductory topic for developers who are building GPU-based Android applications and are interested in experimenting with WebGPU. 
 
 learning_objectives: 
     - Describe the benefits of WebGPU.

From fde881e3ea869b5adb118acf142a339ccc7c096b Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Mon, 2 Dec 2024 12:17:12 -0600
Subject: [PATCH 70/87] Link fixes and updates

---
 .../laptops-and-desktops/intro/find-hardware.md               | 4 +++-
 .../servers-and-cloud-computing/intro/find-hardware.md        | 1 -
 .../lambda_functions/_next-steps.md                           | 4 ++--
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/intro/find-hardware.md b/content/learning-paths/laptops-and-desktops/intro/find-hardware.md
index b1824897e3..88b3d7eb93 100644
--- a/content/learning-paths/laptops-and-desktops/intro/find-hardware.md
+++ b/content/learning-paths/laptops-and-desktops/intro/find-hardware.md
@@ -21,10 +21,12 @@ Windows on Arm laptops can also be used for software development.
 
 ### ChromeOS
 
-Chromebooks with Arm processors can also be used for software development. The Lenovo [Duet 3](https://www.lenovo.com/us/en/p/laptops/lenovo/lenovo-edu-chromebooks/ideapad-duet-3-chromebook-(11-inch,-qlc)/len101i0034) and [Duet 5](https://www.lenovo.com/us/en/p/laptops/lenovo/lenovo-edu-chromebooks/ideapad-duet-5-chromebook/len101i0023) are popular detachable Chromebooks. 
+Chromebooks with Arm processors can also be used for software development. The Lenovo [Duet Gen 9](https://www.lenovo.com/us/en/p/laptops/lenovo/lenovo-edu-chromebooks/lenovo-chromebook-duet-gen-9-11-inch-mediatek/83hh0000us) is a popular detachable Chromebook. 
 
 ### Linux
 
+The System76 [Thelio Astra](https://www.system76.com/arm/) is a powerful Arm-based desktop computer designed by System76 and ships with Ubuntu installed. 
+
 Both Windows and ChromeOS have a Linux subsystem which can be used for software development tasks. Read [WSL for Windows on Arm](/learning-paths/laptops-and-desktops/wsl2) to learn more.
 
 Some single board computers have enough performance to implement Linux based desktop computers. The [Pinebook Pro](https://pine64.org/devices/pinebook_pro/) and the [Raspberry Pi 400](https://www.raspberrypi.com/products/raspberry-pi-400/) are examples.
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/find-hardware.md b/content/learning-paths/servers-and-cloud-computing/intro/find-hardware.md
index 2c7da50571..1a2e5e80ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/find-hardware.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/find-hardware.md
@@ -24,7 +24,6 @@ Cloud providers offer Arm instances based on Neoverse processors. For example:
 Free tier offers are currently available.
 - [Amazon EC2 t4g.small instances powered by AWS Graviton2 processors are free until Dec 31st 2024](https://aws.amazon.com/ec2/instance-types/t4/)
 - [Oracle free tier includes up to 4 instances of ARM Ampere A1 Compute which are always free](https://www.oracle.com/cloud/free/)
-- [Until March 31, 2024 Tau T2A VMs in Google Cloud are available for a free trial](https://cloud.google.com/compute/docs/instances/create-arm-vm-instance#t2afreetrial)
 
 [Get started with Arm-based cloud service platforms](/learning-paths/servers-and-cloud-computing/csp/) explains how to create an account and start an Arm virtual machine using the cloud service providers listed above.
 
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_next-steps.md
index 7460a65db6..00349aa36e 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_next-steps.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_next-steps.md
@@ -26,8 +26,8 @@ further_reading:
         link: https://aws.amazon.com/lambda/getting-started/
         type: documentation
     - resource:
-        title: A Performance Perspective for Graviton Based Lambda Functions
-        link: https://blog.thundra.io/a-performance-perspective-for-graviton-based-lambda-functions
+        title: AWS Lambda performance with Java 21
+        link: https://community.aws/content/2juXXgrDDaUdmi902LHwilBhvNU/aws-lambda-performance-with-java-21-x86-vs-arm64-part-1-initial-measurements-and-comparisons?lang=en
         type: blog
 
 

From 9d1796a2da60660b4c50819ce8ab6e87117f4244 Mon Sep 17 00:00:00 2001
From: Odin Shen Coder <odin.code.shen@gmail.com>
Date: Mon, 2 Dec 2024 20:41:40 +0000
Subject: [PATCH 71/87] Update WPA 1.0.3 installation & add wperf note

---
 content/install-guides/windows-perf-wpa-plugin.md         | 8 ++++----
 .../windowsperf_wpa_plugin/windowsperf_wpa_plugin.md      | 5 +++--
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/content/install-guides/windows-perf-wpa-plugin.md b/content/install-guides/windows-perf-wpa-plugin.md
index 59196a27a0..9afda85d45 100644
--- a/content/install-guides/windows-perf-wpa-plugin.md
+++ b/content/install-guides/windows-perf-wpa-plugin.md
@@ -90,13 +90,13 @@ Alternatively, you can download the `.zip` file from a command prompt:
 ```console
 mkdir wpa-plugin
 cd wpa-plugin
-curl -L -O https://github.com/arm-developer-tools/windowsperf-wpa-plugin/releases/download/1.0.2/wpa-plugin-1.0.2.zip
+curl -L -O https://github.com/arm-developer-tools/windowsperf-wpa-plugin/releases/download/1.0.3/wpa-plugin-1.0.3.zip
 ```
 
 Extract the `.dll` file from the downloaded `.zip` file. 
 
 ```console
-tar -xmf wpa-plugin-1.0.2.zip
+tar -xmf wpa-plugin-1.0.3.zip
 ```
 
 The file `WPAPlugin.dll` is now in your `wpa-plugin` directory. 
@@ -110,7 +110,7 @@ Use the `-addsearchdir` flag to tell `wpa` where to find plugins.
 For example, if you downloaded the `.dll` in your `Downloads` directory, you can run `wpa` as shown below:
 
 ```bash
-wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2
+wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.3
 ```
         
 ##### Option 2: Set an environment variable.
@@ -135,7 +135,7 @@ To open a JSON file directly from the command line, you can use the `-i` flag to
 For example, to open `timeline_long.json` in your  `downloads` directory, run the command:
 
 ```console
-wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.2 -i %USERPROFILE%\Downloads\timeline_long.json
+wpa -addsearchdir %USERPROFILE%\Downloads\wpa-plugin-1.0.3 -i %USERPROFILE%\Downloads\timeline_long.json
 ```
 ## How do I uninstall the WPA plugin?
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
index d2bad14c28..abd47aaa51 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/windowsperf_wpa_plugin.md
@@ -28,6 +28,7 @@ You can save a `.json` output from WindowsPerf by using the `--output` command f
     ```console
     wperf stat -e ld_spec --timeout 5 --json --output example.json
     ```
+    Note: This command won't finish automatically. You can use Ctrl+C to terminate it after it has run for 5-10 seconds.
 
 2. Open Windows Performance Analyzer, and see the following window:
 
@@ -71,10 +72,10 @@ The WPA plugin also generates a graph per event note in order to provide a more
 
 To see all the generated graphs you can expand the `Counting timeline` section in the graph explorer section of WPA.
 
-Run another `wperf` command with different options:
+Before you run another `wperf` command with different options, you use use `wperf -list` to find out supported metrics in your machine.
 
 ```console 
-wperf stat -t -i 0 -n 10 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --timeout 2 --json --output --example3.json
+wperf stat -t -i 0 -n 10 -m imix,l1d_cache_miss_ratio,l1d_cache_mpki,l1d_tlb_miss_ratio,l1d_tlb_mpki -e inst_spec,vfp_spec,ld_spec,st_spec -c 1 --timeout 2 --json --output example3.json
 ```
 
 Note: above command will run for ~20 seconds.

From d3d5d65bc487a3585f5be682548e1969cc6d0f4f Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Mon, 2 Dec 2024 15:52:02 -0600
Subject: [PATCH 72/87] Review Yolo Learning Path

---
 .../microcontrollers/yolo-on-himax/_index.md  | 16 ++--
 .../yolo-on-himax/build-firmware.md           | 34 +++++----
 .../microcontrollers/yolo-on-himax/dev-env.md | 74 ++++++++++++-------
 .../yolo-on-himax/flash-and-run.md            | 18 +++--
 .../yolo-on-himax/web-toolkit.md              | 28 +++++--
 5 files changed, 105 insertions(+), 65 deletions(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index 1dcf71ef7a..f1c4abe72e 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -3,18 +3,18 @@ title: Run a Computer Vision Model on a Himax Microcontroller
 
 minutes_to_complete: 90
 
-who_is_this_for: This is an introduction topic for beginners on how to run a computer vision application on an embedded device from Himax. This example uses an off-the-shelf Himax WiseEye2 module which is based on the Arm Cortex-M55 and Ethos-U55.
+who_is_this_for: This is an introduction topic explaining how to run a computer vision application on an embedded device from Himax. The example uses an off-the-shelf Himax WiseEye2 module which is based on Arm Cortex-M55 and Ethos-U55.
 
 learning_objectives:
-    - Run a you-only-look-once (YOLO) object detection model on the edge device
-    - Build the Himax Software Development Kit (SDK) and generate the firmware image file
-    - Update the firmware on the edge device (Himax WiseEye2)
+    - Run a you-only-look-once (YOLO) object detection model on the Himax device.
+    - Build the Himax Software Development Kit (SDK) and generate the firmware image file.
+    - Update the firmware on the Himax WiseEye2.
 
 prerequisites:
-    - A [Seeed Grove Vision AI Module V2](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html) development board
-    - A [OV5647-62 Camera Module](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html) and included FPC cable
-    - A USB-C cable
-    - An x86 based Linux machine or a machine running Apple Silicon
+    - A [Seeed Grove Vision AI Module V2](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html) development board.
+    - An [OV5647-62 Camera Module](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html) and included FPC cable.
+    - A USB-C cable.
+    - An x86 Linux machine or a Mac running macOS with Apple Silicon.
 
 author_primary: Chaodong Gong, Alex Su, Kieran Hejmadi
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
index ef89acd6d3..b1db98e5f5 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/build-firmware.md
@@ -6,11 +6,15 @@ weight: 3
 layout: learningpathall
 ---
 
-This section will walk you though the process of generating the firmware image file.
+This section explains the process of generating a firmware image file.
 
-## Clone the Himax project
+## Clone the Himax GitHub project
 
-Himax has set up a repository containing a few examples for the Seeed Grove Vision AI V2 board. It contains third-party software and scripts to build and flash the image with the object detection application. By recursively cloning the Himax examples repo, git will include the necessary sub-repositories that have been configured for the project.
+Himax maintains a repository containing a few examples for the Seeed Grove Vision AI V2 board. 
+
+It contains third-party software and scripts to build and flash the image with the object detection application. By recursively cloning the Himax examples repo, git will include the necessary sub-repositories that have been configured for the project.
+
+Clone the repository:
 
 ```bash
 git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git
@@ -19,41 +23,41 @@ cd Seeed_Grove_Vision_AI_Module_V2
 
 ## Compile the firmware
 
-For the object detection to activate, you need to edit the project's `makefile`, located in the `EPII_CM55M_APP_S` directory.
+Use Make to compile the source code for object detection. 
 
-```bash
-cd EPII_CM55M_APP_S
-```
-
-Use the `make` build tool to compile the source code. This should take up to 10 minutes depending on the number of CPU cores available on your host machine. The result is an `.elf` file written to the directory below.
+This takes up to 10 minutes depending on the number of CPU cores available on your host machine. 
 
 ```bash
-make clean
+cd EPII_CM55M_APP_S
 make
 ```
 
+When the build is complete, you have an `.elf` file at `obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf` 
+
 ## Generate the firmware image
 
-The examples repository contains scripts to generate the image file. Copy the `.elf` file to the `input_case1_secboot` directory.
+The examples repository contains scripts to generate the image file. 
+
+Copy the `.elf` file to the `input_case1_secboot` directory.
 
 ```bash
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ```
 
-Run the script corresponding to the OS of your host machine. This will create a file named `output.img` in the `output_case1_sec_wlcsp` directory.
+Run the script your OS as shown below. This will create a file named `output.img` in the `output_case1_sec_wlcsp` directory.
 
 
 {{< tabpane code=true >}}
   {{< tab header="Linux" language="shell">}}
 ./we2_local_image_gen project_case1_blp_wlcsp.json
   {{< /tab >}}
-  {{< tab header="MacOS" language="shell">}}
+  {{< tab header="macOS" language="shell">}}
 ./we2_local_image_gen_macOS_arm64 project_case1_blp_wlcsp.json
   {{< /tab >}}
 {{< /tabpane >}}
 
-Your terminal output should end with the following.
+The script output ends with the following output:
 
 ```output
 Output image: output_case1_sec_wlcsp/output.img
@@ -62,4 +66,4 @@ Output image: output_case1_sec_wlcsp/output.img
 IMAGE GEN DONE
 ```
 
-With this step, you are ready to flash the image onto the Himax development board.
\ No newline at end of file
+You are ready to flash the image onto the Himax development board.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md b/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
index 450297b752..73cb55e14f 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/dev-env.md
@@ -1,48 +1,59 @@
 ---
-title: Set up environment
+title: Set up the environment
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-# Set up the development environment
-
-This learning path has been validated on Ubuntu 22.04 LTS and macOS.
+This Learning Path has been validated on Ubuntu 22.04 LTS and macOS.
 
 {{% notice %}}
-If you are running Windows on your host machine, you can use Ubuntu through Windows subsystem for Linux 2 (WSL2). Check out [this learning path](https://learn.arm.com/learning-paths/laptops-and-desktops/wsl2/setup/) to get started.
+If you are running Windows, you can use Ubuntu through Windows subsystem for Linux 2 (WSL2). Check out [Get started with Windows Subsystem for Linux (WSL) on Arm](https://learn.arm.com/learning-paths/laptops-and-desktops/wsl2/setup/) to learn more.
 {{% /notice %}}
 
-## Install Python, pip and git
+## Install software tools
+
+Follow the instructions below to install the required development tools.
+
+### Install Python and Pip
+
+You will use Python to build the firmware image and pip to install additional dependencies. 
+
+Verify Python is installed by running:
 
-You will use Python to build the firmware image and pip to install some dependencies. Verify Python is installed by running
 ```bash
 python3 --version
 ```
 
-You should see an output like the following.
+You should see an output like the following:
+
 ```output
 Python 3.12.7
 ```
 
-Install `pip` and `venv` with the following commands.
+On Ubuntu, you may need to install `pip` and `venv` with the following commands:
 
 ```bash
 sudo apt update
 sudo apt install python3-pip python3-venv -y
 ```
 
-check the output to verify `pip` is installed correctly.
-```
+Verify Pip is installed correctly:
+
+```bash
 pip3 --version
 ```
 
+The output is similar to:
+
 ```output
-pip 24.2 from /<path-to>/pip (python 3.12)
+pip 24.0 from /usr/lib/python3/dist-packages/pip (python 3.12)
 ```
 
-It is considered good practice to manage `pip` packages through a virtual environment. Create one with the steps below.
+It is good practice to manage Python packages through a virtual environment. 
+
+Create one with the steps below.
 
 ```bash
 python3 -m venv $HOME/yolo-venv
@@ -51,36 +62,43 @@ source $HOME/yolo-venv/bin/activate
 
 Your terminal displays `(yolo-venv)` in the prompt indicating the virtual environment is active.
 
-You will need to have the git version control system installed. Run the command below to verify that git is installed on your system.
+You also need the Git distributed version control system installed. 
+
+Run the command below to verify that Git is installed on your system:
 
 ```bash
 git --version
 ```
 
-You should see output similar to that below.
+If it is installed, you will see output similar to:
 
 ```output
 git version 2.39.3
 ```
 
-## Install make
+### Install Make
 
-Install the make build tool, which is used to build the firmware in the next section.
+Install the Make build tool, which is used to build the firmware in the next section.
 
 {{< tabpane code=true >}}
   {{< tab header="Linux" language="shell">}}
 sudo apt update
 sudo apt install make -y
   {{< /tab >}}
-  {{< tab header="MacOS" language="shell">}}
+  {{< tab header="macOS" language="shell">}}
 brew install make
   {{< /tab >}}
 {{< /tabpane >}}
 
-Successful installation of make will show the following when the `make --version` command is run.
+After Make is installed, run it to print the version.
+
+```bash
+make --version
+```
+
+The output is similar to:
 
 ```output
-$ make --version
 GNU Make 4.3
 Built for x86_64-pc-linux-gnu
 Copyright (C) 1988-2020 Free Software Foundation, Inc.
@@ -88,21 +106,23 @@ License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
 This is free software: you are free to change and redistribute it.
 There is NO WARRANTY, to the extent permitted by law.
 ```
+
 {{% notice Note %}}
-To run this learning path on macOS, you need to verify that your installation is for the GNU Make - not the BSD version.
+If you are using macOS, you need to verify that your installation is for GNU Make - not the BSD version. You should see GNU in the version output.
 {{% /notice %}}
-## Install Arm GNU toolchain
 
-The toolchain is used to compile code from the host to the embedded device architecture.
+### Install the Arm GNU toolchain
+
+The toolchain is used to compile code on the host for the embedded device architecture.
 
 {{< tabpane code=true >}}
-  {{< tab header="Linux" language="shell">}}
+  {{< tab header="x86 Linux" language="shell">}}
 cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz
 export PATH="$HOME/arm-gnu-toolchain-13.2.Rel1-x86_64-arm-none-eabi/bin/:$PATH"
   {{< /tab >}}
-  {{< tab header="MacOS" language="shell">}}
+  {{< tab header="macOS" language="shell">}}
 cd $HOME
 wget https://developer.arm.com/-/media/Files/downloads/gnu/13.3.rel1/binrel/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
 tar -xvf arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi.tar.xz
@@ -111,8 +131,8 @@ export PATH="$HOME/arm-gnu-toolchain-13.3.rel1-darwin-arm64-arm-none-eabi/bin/:$
 {{< /tabpane >}}
 
 {{% notice %}}
-You can add the `export` command to the `.bashrc` file. This was, the Arm GNU toolchain is configured from new terminal sessions as well.
+You can add the `export` command to your `.bashrc` or `.zshrc` file to set the search path for each new shell.
 {{% /notice %}}
 
 
-Now that your development environment is set up, move on to the next section where you will generate the firmware image.
\ No newline at end of file
+Now that your development environment is ready, move to the next section where you will generate the firmware image.
\ No newline at end of file
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
index 78d03dcf5b..43892699b7 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/flash-and-run.md
@@ -6,11 +6,13 @@ weight: 4
 layout: learningpathall
 ---
 
-Now that you have generated an image file on the local host machine, you are ready to flash the microcontroller with this firmware.
+Now that you have generated an image file on the local host machine, you are ready to flash the microcontroller with the firmware.
 
 ## Install xmodem
 
-`Xmodem` is a basic file transfer protocol which is easily installed using the Himax examples repository. Run the following command to install the dependency. If you cloned the repository to a different location, replace $HOME with the path.
+`Xmodem` is a basic file transfer protocol which is easily installed using the Himax examples repository. 
+
+Run the following command to install the dependency:
 
 ```bash
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2
@@ -19,11 +21,13 @@ pip install -r xmodem/requirements.txt
 
 ## Connect the module
 
-To prepare for the next steps, it's time to get the board set up. Insert the Flexible printed circuit (FPC) into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
+It's time to get the board set up. 
+
+Insert the Flexible printed circuit (FPC) into the Grove Vision AI V2 module. Lift the dark grey latch on the connector as per the image below.
 
 ![unlatched](./unlatched.jpg)
 
-Then, slide the FPC connector in with the metal pins facing down and close the dark grey latch to fasten the connector.
+Slide the FPC connector in with the metal pins facing down and close the dark grey latch to fasten the connector.
 
 ![latched](./latched.jpg)
 
@@ -56,11 +60,11 @@ chmod 0777 <COM port>
 ```
 {{% /notice %}}
 
-The full path to the port is needed in the next step, so be sure to note it down.
+The full path to the port is needed in the next step, so be sure to save it. 
 
 ## Flash the firmware onto the module
 
-Run the python script below to flash the firmware.
+Run the python script below to flash the firmware:
 
 ```bash
 python xmodem\xmodem_send.py --port=<COM port> \
@@ -72,7 +76,7 @@ python xmodem\xmodem_send.py --port=<COM port> \
 When you run other example models demonstrated in the later section [Run additional models in the web toolkit](/learning-paths/microcontrollers/yolo-on-himax/web-toolkit/), you need to adapt this command with `--model` argument.
 {{% /notice %}}
 
-After the firmware image burning is completed, the message `Do you want to end file transmission and reboot system? (y)` is displayed. Press the reset button indicated in the image below.
+After the firmware image flashing is completed, the message `Do you want to end file transmission and reboot system? (y)` is displayed. Press the reset button shown in the image below.
 
 ![reset button](./reset_button.jpg)
 
diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
index f3ec67106b..e03500d02a 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/web-toolkit.md
@@ -8,15 +8,19 @@ layout: learningpathall
 
 In this section, you will view a live camera feed with a computer vision application running.
 
-## Modify the Makefile
+## Modify the makefile
 
-Change the directory to the where the Makefile is located. If you cloned the repository to a different location, replace $HOME with the path.
+Change to the directory where the `makefile` is located. 
 
 ```bash
 cd $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/
 ```
 
-The table shows the different options available to use with the web toolkit. Modify the `APP_TYPE` field in the `makefile` to one of the values in the table. Then pass the `--model` argument to the python `xmodem` command.
+The table shows the different options available to use with the web toolkit. 
+
+Use a text editor to open `makefile` and modify the `APP_TYPE` field to one of the values in the table. 
+
+You will use the model argument value with the `--model` option to the `xmodem` command.
 
 |APP_TYPE           |Description        | Model argument |
 |---                |---                |---
@@ -28,22 +32,22 @@ For `tflm_fd_fm`, you need to pass all three models as separate `--model` argume
 {{% /notice %}}
 
 
-
 ## Regenerate the firmware image
 
-Now you can run `make` to re-generate the `.elf` file.
+Use Make to re-generate the `.elf` file.
 
 ```bash
 make clean
 make
 ```
 
-Use the commands from [Flash firmware onto the microcontroller](/learning-paths/microcontrollers/yolo-on-himax/flash-and-run/) section to run re-generate the firmware image.
+Use the same commands from previous section to run re-generate the firmware image:
 
 ```bash
 cd ../we2_image_gen_local/
 cp ../EPII_CM55M_APP_S/obj_epii_evb_icv30_bdv10/gnu_epii_evb_WLCSP65/EPII_CM55M_gnu_epii_evb_WLCSP65_s.elf input_case1_secboot/
 ```
+
 Run the script corresponding to the OS of your host machine.
 
 {{< tabpane code=true >}}
@@ -76,11 +80,19 @@ wget https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2/release
 unzip Himax_AI_web_toolkit.zip
 ```
 
-Open the unzipped directory in your file browsing system and double click `index.html`. This will open the GUI within your default browser.
+{{% notice Note %}}
+If needed, install the unzip command:
+
+```bash
+sudo apt install unzip -y
+```
+{{% /notice %}}
+
+Open the file `index.html` in a browser. You can double click the file in a file browser or use the `File -> Open File...` command from the browser menu.
 
 ## Connect to the Grove Vision AI
 
-Select `Grove Vision AI(V2)` in the top-right hand corner and press `Connect` button. Follow the instructions to set up the connection. Now you should see a video feed with a bounding box showing identified objects, poses or face detection.
+Select `Grove Vision AI(V2)` in the top-right hand corner and press `Connect` button. Follow the instructions to set up the connection. You should see a video feed with a bounding box showing identified objects, poses or face detection.
 
 ![Himax web UI](./himax_web_ui.jpg)
 

From 4a9af5eecacf0cbcc83aa4c89222b04bdfd1b366 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Mon, 2 Dec 2024 19:30:58 -0600
Subject: [PATCH 73/87] Update Arm Compiler for Linux install guide

---
 content/install-guides/acfl.md | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/content/install-guides/acfl.md b/content/install-guides/acfl.md
index 383cde57a9..cf070251b5 100644
--- a/content/install-guides/acfl.md
+++ b/content/install-guides/acfl.md
@@ -142,6 +142,33 @@ install takes place **after** ACfL, you will no longer be able to fully
 uninstall ACfL.
 {{% /notice %}}
 
+## Download and install using System Packages - Ubuntu Linux
+
+Arm Compiler for Linux is available to install with the Ubuntu system package manager `apt` command.
+
+### Setup the ACfL package repository:
+
+Add the ACfL `apt` package repository to your Ubuntu 20.04 or 22.04 system:
+
+```bash { target="ubuntu:latest" }
+sudo apt update
+sudo apt install -y curl
+source /etc/os-release
+curl "https://developer.arm.com/packages/ACfL%3A${NAME}-${VERSION_ID/%.*/}/${VERSION_CODENAME}/Release.key" | sudo tee /etc/apt/trusted.gpg.d/developer-arm-com.asc
+echo "deb https://developer.arm.com/packages/ACfL%3A${NAME}-${VERSION_ID/%.*/}/${VERSION_CODENAME}/ ./" | sudo tee /etc/apt/sources.list.d/developer-arm-com.list
+sudo apt update
+```
+
+The ACfL Ubuntu package repository is now ready to use.
+
+### Install ACfL
+
+Download and install Arm Compiler for Linux with:
+
+```bash { target="ubuntu:latest" }
+sudo apt install acfl
+```
+
 ### Set up environment
 
 Arm Compiler for Linux uses environment modules to dynamically modify your user environment. Refer to the [Environment Modules documentation](https://lmod.readthedocs.io/en/latest/#id) for more information.

From c329faadbfc9ef25a4c2394e17066e56f341c8cc Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 10:05:51 +0000
Subject: [PATCH 74/87] Editorial.

---
 .../1-webgpu-fundamentals.md                  | 20 ++++++-------
 .../android_webgpu_dawn/2-env-setup.md        | 11 ++++----
 .../android_webgpu_dawn/3-integrate-dawn.md   | 28 +++++++++----------
 .../6-render-a-simple-3D-object-part-2.md     |  2 +-
 .../7-Profiling-App-using-Streamline.md       |  8 +++---
 5 files changed, 34 insertions(+), 35 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
index 735e056a7b..c5cd651c18 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
@@ -50,27 +50,27 @@ Any limitations of the API caused by privacy constraints can be disabled when us
 
 The initial focus for WebGPU was JavaScript.  The initial `webgpu.h` header file is written in C. 
 
-This Learning Path uses C++ rather than JavaScript or C for the following reasons:
+This Learning Path uses C++ rather than JavaScript or C, for the following reasons:
 
 * C++ remains the primary language used for high performance graphics applications, such as video games, render engines, and modeling tools.
-* The level of abstraction and control of C++ is well suited for interacting with graphics APIs in general.
+* The level of abstraction and control of C++ is well-suited for interacting with graphics APIs in general.
 * Graphics programming is a good way to improve skills in C++.
 
 ## Dawn: the Google WebGPU implementation
 
-Since WebGPU is a standard and not an implementation itself, there are various possible implementations. 
+Since WebGPU is a standard and not an implementation itself, there are various possible options for implementation. 
 
-[Dawn](https://github.com/google/dawn) is an open-source, cross-platform implementation of the WebGPU standard. 
+[Dawn](https://github.com/google/dawn) is an open-source, cross-platform implementation of the WebGPU standard. It implements the WebGPU functionality specified in `webgpu.h`. 
 
-It implements the WebGPU functionality specified in `webgpu.h`. Dawn is designed to be integrated as part of a larger system such as Chromium or a native Android Application.
+Dawn is designed to be integrated as part of a larger system such as Chromium or a native Android Application.
 
 Dawn provides several WebGPU building blocks:
 
-* WebGPU C/C++ headers that applications and other building blocks use, including a header file and C++ wrapper.
-* A "native" implementation of WebGPU using appropriate APIs: D3D12, Metal, Vulkan, and OpenGL. 
-* A client-server implementation of WebGPU for applications that are in a sandbox without access to native drivers.
-* Tint; a compiler for the WebGPU Shader Language (WGSL), that converts shaders to and from WGSL.
+* WebGPU C/C++ headers that applications and other building blocks use, including a header file and a C++ wrapper.
+* A "native" implementation of WebGPU using appropriate APIs, such as D3D12, Metal, Vulkan, and OpenGL. 
+* A client-server implementation of WebGPU, for applications that are in a sandbox without access to native drivers.
+* Tint, a compiler for the WebGPU Shader Language (WGSL), that converts shaders to and from WGSL.
 
-Because it is written in C++, Dawn provides enhanced error message reporting and logging. The fact that it is open source, also means that it is easier to inspect stack traces when applications crash.
+As it is written in C++, Dawn provides enhanced error message reporting and logging. The fact that it is open source, also means that it is easier to inspect stack traces when applications crash.
 
 Dawn is usually ahead of `wgpu-native`, another WebGPU implementation, in terms of new functionality developments and standards changes. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
index 3f566eeda7..43211b4c33 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/2-env-setup.md
@@ -29,9 +29,10 @@ You can use any computer and operating system that supports this software.
 ![SDK Platforms #center](images/sdk-platforms.png "Figure 2: SDK Platforms")
 
 6. In the **SDK Tools** tab, select the following:
-    * Select **Android SDK Build-Tools 35**.
-    * Select **NDK (Side by side)**.
-    * Select **CMake**.
+    
+    * **Android SDK Build-Tools 35**.
+    * **NDK (Side by side)**.
+    * **CMake**.
 
 ![SDK Tools #center](images/sdk-tools.png "Figure 3: SDK Tools")
 
@@ -41,7 +42,7 @@ Click **OK** to install and update the selected components.
 
 Profiling is an important step in the Android application development cycle. 
 
-The default profiler in Android Studio is great to profile CPU-related metrics, but does not provide GPU details. 
+The default profiler in Android Studio is great to profile CPU-related metrics, but it does not provide GPU details. 
 
 Arm Performance Studio is a comprehensive profiling tool to profile both CPUs and GPUs. 
 
@@ -55,7 +56,7 @@ One of the components of Performance Studio is Streamline. Streamline captures d
 Install Arm Performance Studio using the [Arm Performance Studio Install Guide](/install-guides/ams/).
 
 {{% notice Tip %}}
-If you want to learn more about Arm Performance Studio and Streamline before continuing, refer to [Get started with Arm Performance Studio for mobile](https://learn.arm.com/learning-paths/smartphones-and-mobile/ams/ams/).
+To learn more about Arm Performance Studio and Streamline, see [Get started with Arm Performance Studio for mobile](https://learn.arm.com/learning-paths/smartphones-and-mobile/ams/ams/).
 {{% /notice %}}
 
 Android Studio and Arm Performance Studio are now installed, and you are ready to create a WebGPU Android application. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
index 4228762ef0..b895a1975c 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
@@ -6,19 +6,19 @@ weight: 4
 layout: learningpathall
 ---
 
-## Set up Android Project
+## Set up your Android Studio Project
 
 Start by creating a new Android Studio project.
 
-Open Android Studio, click **New Project** and select **Game Activity (C++)** as shown below:
+Open Android Studio, click **New Project**, and select **Game Activity (C++)** as shown below:
 
 ![New Game Activity #center](./images/android_studio_new_game_activity.png "Figure 4: New C++ Game Activity")
 
-Set the **Name** field to **dawnwebgpu**. 
+* Set the **Name** field to **dawnwebgpu**. 
 
-Click **Next** to continue. 
+* Click **Next** to continue. 
 
-Finish the new project creation by accepting all the default selections until the project is created. 
+* Finish setting up the new project by accepting all the default selections until the project is created. 
 
 The project is created in `~/AndroidStudioProjects`.
 
@@ -61,27 +61,25 @@ Unzip the project files:
 unzip main.zip
 ```
 
-You now have a directory named **Android_DawnWebGPU-main** in your **webgpu-files** directory. 
+You now have a directory named **Android_DawnWebGPU-main** in the **webgpu-files** directory. 
 
-During the following sections you will copy some of the required files from the **Android_DawnWebGPU-main** directory into your Game Activity project to learn how to create WebGPU applications.
+During the following sections, you will copy some of the required files from the **Android_DawnWebGPU-main** directory into your Game Activity project to learn how to create WebGPU applications.
 
 ## Upgrade the application to include Dawn
 
 Return to Android Studio and start work on the WebGPU application. 
 
-The Android Game Activity framework uses OpenGLES3 for graphics. 
-
-You can remove this dependency and replace it with WebGPU. 
+The Android Game Activity framework uses OpenGLES3 for graphics. You can remove this dependency and replace it with WebGPU. 
 
 Add WebGPU to the project using the following steps:
 
-1. In Android Studio, navigate to the project view and find the **app** --> **cpp** folder.
+* In Android Studio, navigate to the project view, and find the **app** --> **cpp** folder.
 
-2. Open the terminal in Android Studio. You are now in the **dawnwebgpu** directory.
+* Open the terminal in Android Studio. You are now in the **dawnwebgpu** directory.
 
-3. Create a new directory and download the WebGPU header file from GitHub.
+* Create a new directory and download the WebGPU header file from GitHub.
 
-4. Run the commands below to download the `webgpu.hpp` header file:
+* Run the commands below to download the `webgpu.hpp` header file:
 
 ```console
 mkdir -p app/src/main/cpp/webgpu/include/webgpu
@@ -90,7 +88,7 @@ cp ~/webgpu-files/Android_DawnWebGPU-main/app/src/main/cpp/webgpu/include/webgpu
 cd ../..
 ```
 
-5. Next copy the remaining WebGPU files to your project. 
+Next, copy the remaining WebGPU files to your project. 
 
 ```console
 cp ~/webgpu-files/Android_DawnWebGPU-main/app/src/main/cpp/webgpu/CMakeLists.txt .
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
index 04c8f30cf7..cfa1209825 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
@@ -174,6 +174,6 @@ For example:
 
 Now click the **Run** icon in Android Studio, which builds the application and launches it on the connected device, producing the following output:
 
-![Output #center](images/output.gif  "Output")
+![Output #center](images/output.gif  "Figure 10: Output")
 
 Congratulations! You are run a WebGPU application on an Android device.
\ No newline at end of file
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index c47fbbaba8..27f638fd8a 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -14,11 +14,11 @@ Follow these steps to configure Arm Streamline Performance Analyzer to capture M
 * Navigate to the **Start** tab, select **Android (adb)**, select the device, and then select the application to debug.
 * Select **Select Counters**.
 
-![Select device #center](images/streamline_select.png "Select device")
+![Select device #center](images/streamline_select.png "Figure 11: Select device")
 
 This opens a new window:
 
-![Select counters #center](images/streamline_select_counters.png "Select counters")
+![Select counters #center](images/streamline_select_counters.png "Figure 12: Select counters")
 
 * Search for **Mali Timeline Events: Perfetto**.
 * Check that it is listed in **Events to collect**.
@@ -36,13 +36,13 @@ Wait until Streamline completes processing the data.
 
 Switch to **Mali Timeline** view as shown below:
 
-!["Mali Timeline Streamline" #center](images/Streamline-mali-timeline.png "Mali Timeline Streamline")
+!["Mali Timeline Streamline" #center](images/Streamline-mali-timeline.png "Figure 13: Mali Timeline Streamline")
 
 You might have to zoom into the data up to the maximum (**500 us**), as you are rendering a simple 3D object. 
 
 You can analyze two consecutive frames as shown below:
 
-!["Two consecutive frames" #center](./images/Streamline-mali-analysis.png "Two consecutive frames")
+!["Two consecutive frames" #center](./images/Streamline-mali-analysis.png "Figure 14: Two consecutive frames")
 
 Arm has worked with the Dawn team to optimize data uploading to GPU buffers for Mali GPUs. 
 

From 307c16eceb9cfa756ea9bb74590af68ffd957191 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 10:20:21 +0000
Subject: [PATCH 75/87] Editorial.

---
 .../android_webgpu_dawn/3-integrate-dawn.md            | 10 ++++------
 .../android_webgpu_dawn/4-using-webgpu-apis.md         | 10 ++++++----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
index b895a1975c..2fc0518191 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
@@ -15,9 +15,7 @@ Open Android Studio, click **New Project**, and select **Game Activity (C++)** a
 ![New Game Activity #center](./images/android_studio_new_game_activity.png "Figure 4: New C++ Game Activity")
 
 * Set the **Name** field to **dawnwebgpu**. 
-
 * Click **Next** to continue. 
-
 * Finish setting up the new project by accepting all the default selections until the project is created. 
 
 The project is created in `~/AndroidStudioProjects`.
@@ -106,14 +104,14 @@ WebGPU is a constantly evolving standard and hence its implementation, Dawn is a
 
 To add Dawn to our application, there are two options:
 
-1. Create a shared/static library from the Dawn source and use it in application.
-2. Download the source as a dependency and build it as part of the project build.
+* Create a shared/static library from the Dawn source and use it in application.
+* Download the source as a dependency and build it as part of the project build.
 
 You will use the second option here, since it provides more flexibility for debug.
 
 The files `webgpu/webgpu.cmake` and `CMakeLists.txt` facilitate downloading and building WebGPU with Dawn implementation and integrating Dawn into the project.
 
-4. Add WebGPU to the project.
+## Add WebGPU to the project
 
 WebGPU is added to the project in the file `CMakeLists.txt`.
 
@@ -153,6 +151,6 @@ target_link_libraries(dawnwebgpu
 ```
 
 
-The `webgpu.hpp` header file acts like an interface, exposing all WebGPU functions and variables to the main Application.
+The `webgpu.hpp` header file acts like an interface, exposing all the WebGPU functions and variables to the main Application.
 
 Navigate to the next section to continue building the WebGPU application. 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
index 1e4c9eacfc..c30e73e3bd 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
@@ -8,13 +8,15 @@ layout: learningpathall
 
 ## Project Setup
 
-With the `webgpudawn` library integrated, you can start by removing the extra files included as part of the stock Game Activity project. 
+With the `webgpudawn` library integrated, you can begin removing the extra files included as part of the stock Game Activity project. 
 
-1. Delete all the files from the top `cpp` directory except `CMakeLists.txt`. 
+* Delete all the files from the top `cpp` directory, except `CMakeLists.txt`. 
 
-    You have already reviewed `CMakeLists.txt` in the previous section. 
+    {{% notice Note %}}
+You have already reviewed `CMakeLists.txt` in the previous section. 
+{{% /notice %}}
 
-2. Add the files `webgpuRenderer.cpp` and `webgpuRenderer.h` files for the WebGPU application. 
+* Add the files `webgpuRenderer.cpp` and `webgpuRenderer.h` files for the WebGPU application. 
 
     Run the commands below to add a new `main.cpp` and WebGPU renderer files:
 

From 28bc46b811fc245a0b97ded395c34c70a2552e0e Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 10:32:39 +0000
Subject: [PATCH 76/87] Editorial.

---
 .../android_webgpu_dawn/3-integrate-dawn.md   |  3 --
 .../5-render-a-simple-3D-object-part-1.md     | 35 +++++++++----------
 .../6-render-a-simple-3D-object-part-2.md     |  2 +-
 .../7-Profiling-App-using-Streamline.md       |  6 ++--
 4 files changed, 21 insertions(+), 25 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
index 2fc0518191..ca5be128bb 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
@@ -72,11 +72,8 @@ The Android Game Activity framework uses OpenGLES3 for graphics. You can remove
 Add WebGPU to the project using the following steps:
 
 * In Android Studio, navigate to the project view, and find the **app** --> **cpp** folder.
-
 * Open the terminal in Android Studio. You are now in the **dawnwebgpu** directory.
-
 * Create a new directory and download the WebGPU header file from GitHub.
-
 * Run the commands below to download the `webgpu.hpp` header file:
 
 ```console
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
index 24416a80ff..e8fc427000 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
@@ -69,43 +69,42 @@ The Render Pipeline has two main types of stages, **fixed-function** and **progr
 
 The pipeline description consists of the following steps:
 
-* Describe vertex pipeline state
-* Describe vertex pipeline state
-* Describe primitive pipeline state
-* Describe fragment pipeline state
-* Describe stencil/depth pipeline state
-* Describe multi-sampling state
-* Describe pipeline layout
+* Describe the vertex pipeline state.
+* Describe the primitive pipeline state.
+* Describe the fragment pipeline state.
+* Describe the stencil/depth pipeline state.
+* Describe the multi-sampling state.
+* Describe the pipeline layout.
 
-The fixed function stages are well documented and you can refer to [code](https://github.com/varunchariArm/Android_DawnWebGPU/blob/main/app/src/main/cpp/webgpuRenderer.cpp#L256) and [further reading](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/hello-triangle.html#lit-24) for configuring them. 
+The fixed function stages are well-documented, and you can refer to [code](https://github.com/varunchariArm/Android_DawnWebGPU/blob/main/app/src/main/cpp/webgpuRenderer.cpp#L256) and [further reading](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/hello-triangle.html#lit-24) for information about configuring them. 
 
-Configuring these stages is straight forward and is similar to other graphics APIs.
+Configuring these stages is straightforward and is similar to other graphics APIs.
 
 ### Programmable stage
 
-There are two programmable stages, vertex and fragment programmable stages. Both of them uses **Shader Module**.
+There are two programmable stages, vertex, and fragment programmable stages. Both of them use the **Shader Module**.
 
 ### Shaders
 
 Both the vertex and fragment programmable stages can use the same shader module or have individual shader modules. 
 
-The Shader module is kind of a dynamic library (like a .dll, .so or .dylib file), except that it talks the binary language of your GPU rather than your CPU.
+The Shader module is like a dynamic library (such as a .dll, .so, or a .dylib file), except that it uses the binary language of your GPU rather than that of your CPU.
 
 ### Shader Code
 
 The shader language officially used by WebGPU is called WebGPU Shading Language, [WGSL](https://gpuweb.github.io/gpuweb/wgsl/). 
 
-All implementations of WebGPU support it, and Dawn also offers the possibility to provide shaders written in [SPIR-V](https://www.khronos.org/spir).
+All implementations of WebGPU support it, and Dawn also offers the opportunity to provide shaders written in [SPIR-V](https://www.khronos.org/spir).
 
 {{% notice Note %}}
-WGSL was originally designed to be a human-editable version of SPIR-V programming model, so transpilation from SPIR-V to WGSL is in theory efficient and lossless. You can use [Naga](https://github.com/gfx-rs/naga) or [Tint](https://dawn.googlesource.com/tint) to translate.
+WGSL was originally designed to be a human-editable version of the SPIR-V programming model, so transpilation from SPIR-V to WGSL is in theory efficient and lossless. You can use [Naga](https://github.com/gfx-rs/naga) or [Tint](https://dawn.googlesource.com/tint) to translate.
 {{% /notice %}}
 
-It is highly recommended to understand WGSL syntax and capabilities to better program in WebGPU.
+It is recommended that you learn about the WGSL syntax and capabilities to better program in WebGPU.
 
 ### Shader Module Creation
 
-It is simple to create a Shader module in WebGPU:
+It is simple to create a Shader module in WebGPU. Use the following code:
 
 ```C++
 ShaderModuleDescriptor shaderDesc;
@@ -116,15 +115,15 @@ By default the `nextInChain` member of `ShaderModuleDescriptor` is a `nullptr`.
 
 The `nextInChain` pointer is the entry point of WebGPU’s extension mechanism. It is either null or pointing to a structure of type `WGPUChainedStruct`. 
 
-It may recursively have a next element (again, either null or pointing to some `WGPUChainedStruct`). 
+It may recursively have a next element (again, either null or pointing to a `WGPUChainedStruct`). 
 
-Second, it has a struct type `sType`, which is an enum telling in which struct the chain element can be cast.
+Secondly, it has the struct type `sType`, which is an enum reporting in which struct the chain element can be cast.
 
 To create a shader module from WGSL code, use the `ShaderModuleWGSLDescriptor` SType. 
 
 In Dawn, a SPIR-V shader can similarly be created using the `WGPUShaderModuleSPIRVDescriptor`.
 
-The field shaderCodeDesc.chain corresponds to the chained struct when cast as a simple `WGPUChainedStruct`, which must be set to the corresponding SType enum value:
+The field shaderCodeDesc.chain corresponds to the chained struct when cast as a simple `WGPUChainedStruct`, that must be set to the corresponding SType enum value:
 
 ```C++
 ShaderModuleWGSLDescriptor shaderCodeDesc;
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
index cfa1209825..a71370ba67 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
@@ -176,4 +176,4 @@ Now click the **Run** icon in Android Studio, which builds the application and l
 
 ![Output #center](images/output.gif  "Figure 10: Output")
 
-Congratulations! You are run a WebGPU application on an Android device.
\ No newline at end of file
+Congratulations! You have now run a WebGPU application on an Android device.
\ No newline at end of file
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index 27f638fd8a..311fcc1d51 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -30,7 +30,7 @@ Once you have selected the device, the application, and the metrics to be collec
 
 This automatically starts the application and begins collecting the profiling data. 
 
-Make sure the application is running correctly on your Android device. After a few seconds, you can stop the capture. 
+Make sure the application is running correctly on your Android device. After a few seconds, you can stop the capture process. 
 
 Wait until Streamline completes processing the data. 
 
@@ -48,11 +48,11 @@ Arm has worked with the Dawn team to optimize data uploading to GPU buffers for
 
 Arm has implemented a **Fast Path** mechanism where the Vertex Queue starts processing in parallel while an earlier Fragment Queue is simultaneously being processed. 
 
-As you can see from the above picture, there is some overlap between Fragment Queue of first frame and Vertex Queue of the consecutive frame. 
+As you can see from the above picture, there is some overlap between the Fragment Queue of first frame, and Vertex Queue of the consecutive frame. 
 
 This demonstrates that the application is hitting the **Fast Path** that Arm has implemented to optimize performance of Dawn for Mali GPUs. 
 
-The overlap is small since the application is rendering the same simple 3D object under different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This will demonstrate the overlap in more detail.
+The overlap is small since the application is rendering the same simple 3D object under a different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This demonstrates the overlap in more detail.
 
 {{% notice Tip %}}
 You can experiment with different counters in Streamline and also explore other CPU profiling data.

From c25036c715525df370ca2483ce2d6dd0ef960dc0 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 10:58:55 +0000
Subject: [PATCH 77/87] Editorial.

---
 .../5-render-a-simple-3D-object-part-1.md                   | 2 +-
 .../6-render-a-simple-3D-object-part-2.md                   | 2 +-
 .../android_webgpu_dawn/7-Profiling-App-using-Streamline.md | 6 +++---
 .../smartphones-and-mobile/android_webgpu_dawn/_review.md   | 6 +++---
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
index e8fc427000..67e7f82d17 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
@@ -31,7 +31,7 @@ If you would like to learn more, refer to the [Command Queue](https://eliemichel
 WebGPU is a simple system. It runs three types of functions on the GPU: 
 
 * A Vertex Shader that computes vertices. The shader returns vertex positions.
-* A Fragment Shader that computes colors. The shader indirectly writes data to textures. That data does not have to be limited to colors.
+* A Fragment Shader that computes colors. When an object is drawn, for each pixel to be drawn the GPU calls the fragment shader. The fragment shader then returns a color.
 * A Compute Shader that is more generic. It is effectively a function that you can call and request to execute as many times as you require.
 
 Here is a simplified diagram of a WebGPU setup to draw triangles by using a vertex shader and a fragment shader:
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
index a71370ba67..54d6e1c638 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
@@ -35,7 +35,7 @@ The file format is not complex, but parsing files is out of the scope of this Le
 You can use open-source software such as Blender to create your own 3D objects.
 
 {{% notice Note %}}
-Exactly one of your source files must define `TINYOBJLOADER_IMPLEMENTATION` before including it:
+Only one of your source files must define `TINYOBJLOADER_IMPLEMENTATION` before including it:
 
 ```C++
 #define TINYOBJLOADER_IMPLEMENTATION // add this to exactly 1 of your C++ files
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index 311fcc1d51..5892932817 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Set up Arm Streamline
 
-Follow these steps to configure Arm Streamline Performance Analyzer to capture Mali GPU related data:
+Follow these steps to configure Arm Streamline Performance Analyzer to capture Mali GPU-related data:
 
 * Confirm that your Android device is connected to the development machine.
 * Navigate to the **Start** tab, select **Android (adb)**, select the device, and then select the application to debug.
@@ -48,11 +48,11 @@ Arm has worked with the Dawn team to optimize data uploading to GPU buffers for
 
 Arm has implemented a **Fast Path** mechanism where the Vertex Queue starts processing in parallel while an earlier Fragment Queue is simultaneously being processed. 
 
-As you can see from the above picture, there is some overlap between the Fragment Queue of first frame, and Vertex Queue of the consecutive frame. 
+As you can see from the above picture, there is some overlap between the Fragment Queue of first frame, and the Vertex Queue of the consecutive frame. 
 
 This demonstrates that the application is hitting the **Fast Path** that Arm has implemented to optimize performance of Dawn for Mali GPUs. 
 
-The overlap is small since the application is rendering the same simple 3D object under a different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This demonstrates the overlap in more detail.
+The overlap is small as the application is rendering the same simple 3D object under a different orientation. You can extend the application to render complex objects with multiple *Uniform Buffers*. This demonstrates the overlap in more detail.
 
 {{% notice Tip %}}
 You can experiment with different counters in Streamline and also explore other CPU profiling data.
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
index 6d4d9d6d3a..25383d3999 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_review.md
@@ -9,7 +9,7 @@ review:
             - APIs designed for cloud-based applications.
         correct_answer: 2                    
         explanation: >
-            WebGPU is a Render Hardware Interface built on top of the various APIs provided by the driver/OS depending on your platform. This duplicated development effort is made once by the web browsers and made available to us through the webgpu.h header they provide
+            WebGPU is a Render Hardware Interface built on top of the various APIs provided by the driver/OS depending on your platform. This duplicated development effort is made once by the web browsers and made available through the webgpu.h header that they provide.
 
     - questions:
         question: >
@@ -20,7 +20,7 @@ review:
             - A new programming language to program GPUs.
         correct_answer: 1                   
         explanation: >
-            Dawn is an open-source and cross-platform implementation of the WebGPU standard, lead by Google. More precisely it implements webgpu.h that is a one-to-one mapping with the WebGPU IDL. 
+            Dawn is an open-source and cross-platform implementation of the WebGPU standard, lead by Google. More precisely, it implements webgpu.h that is a one-to-one mapping with the WebGPU IDL. 
                
     - questions:
         question: >
@@ -31,7 +31,7 @@ review:
             - A comprehensive profiling software to profile both CPUs and GPUs.
         correct_answer: 3          
         explanation: >
-            Streamline is an application profiler that can capture data from multiple sources, including Program Counters (PC), Samples from the hardware Performance Monitoring Unit (PMU) counters in the Arm CPU, Arm® Mali™ GPUs, and Arm Immortalis™ GPUs.
+            Streamline is an application profiler that can capture data from multiple sources, including Program Counters (PC), samples from the hardware Performance Monitoring Unit (PMU) counters in the Arm CPU, Arm® Mali™ GPUs, and Arm Immortalis™ GPUs.
 
 
 

From d2a79f1833d7ae584a2174d83a0b684b3998c02f Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 11:25:59 +0000
Subject: [PATCH 78/87] Editorial.

---
 .../4-using-webgpu-apis.md                    | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
index c30e73e3bd..1a2ebaf79c 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
@@ -50,27 +50,27 @@ It is useful to understand these layers as you begin to use WebGPU APIs in an ap
 
 ### The adapter
 
-Before requesting access to a **device**, you need to select an **adapter**. 
+Before requesting access to a device, you need to select an adapter. 
 
-The same host system may expose multiple adapters if it has access to multiple physical GPUs. It may also have an adapter that represents an emulated/virtual device. Each adapter advertises a list of optional **features** and **supported limits** that it can handle. 
+The same host system might expose multiple adapters if it has access to multiple physical GPUs. It might also have an adapter that represents an emulated/virtual device. Each adapter offers optional features and supported limits that it can handle. 
 
-These are used to determine the overall capabilities of the system before **requesting the device**. The **adapter** is used to **access the capabilities** of the user’s hardware, which are used to select the behavior of your application among very different code paths. 
+You can use these features and limits to determine the overall capabilities of the system before requesting the device. The adapter is used to access the capabilities of the user’s hardware, which are used to select the behavior of your application among different code paths. 
 
-Once a code path is chosen, a device is created with the chosen capabilities. Only the capabilities selected for this device are  allowed in the rest of the application. This way, it is **not** possible to inadvertently rely on capabilities specific to a device.
+Once a code path is chosen, a device is created with the chosen capabilities. Only the capabilities selected for this device are  allowed in the rest of the application. This way, it is notpossible to inadvertently rely on capabilities specific to a device.
 
-![Supported Limits #center](images/adapter_supported_limits.png "Figure 7: Adapter Supported Limits")
+![Supported Limits #center](images/adapter_supported_limits.png "Figure 7: Adapter Supported Limit s")
 
 {{% notice Tip %}}
-In an advanced use of the adapter/device duality, you can set up multiple limit presets and select one depending on the adapter. 
+To use the adapter/device duality in an advanced way, you can set up multiple limit presets and select one depending on the adapter. 
 
-In this case, there is a single preset and abort early if it is not supported.
+In this scenario, there is a single preset and an abort early option if it is not supported.
 {{% /notice %}}
 
 ### Requesting the adapter
 
 An adapter is not something you create, but rather something that you *request* using the function `requestAdapter()`.
 
-Before doing that you need to create an instance using the `createInstance()` function. 
+Before doing this you need to create an instance using the `createInstance()` function. 
 
 ```C++
 wgpu::Instance instance = createInstance(InstanceDescriptor{});
@@ -80,7 +80,7 @@ In order to display something on the screen, the operating system needs to provi
 
 The Game Activity provides a *pApp* member which exposes an Android Window. WebGPU can use an Android Window for rendering. 
 
-WebGPU cannot use the *window* directly, but uses something called **a surface**, which can be easily created using the window. 
+WebGPU cannot use the *window* directly, but uses something called a "surface", which can be easily created using the window. 
 
 ```C++
 wgpu::SurfaceDescriptorFromAndroidNativeWindow platformSurfaceDescriptor = {};
@@ -131,7 +131,7 @@ There are few options to set the limits:
 wgpu::RequiredLimits requiredLimits = Default;
 ```
 
-* Query the Adapter's *supported limits* and use them as *required limits*:
+* Query the Adapter's *supported limits*, and use them as *required limits*:
 
 ```C++
 wgpu::SupportedLimits supportedLimits;
@@ -140,7 +140,7 @@ wgpu::RequiredLimits requiredLimits = Default;
 requireLimits.limits = supportedLimits.limits;
 ```
 
-* Query the Adapter's *supported limits* and define specific *better* limits in the *required limits*:
+* Query the Adapter's *supported limits*, and define specific *better* limits in the *required limits*:
 
 ```C++
 wgpu::SupportedLimits supportedLimits;
@@ -155,9 +155,9 @@ requiredLimits.limits.minUniformBufferOffsetAlignment = supportedLimits.limits.m
 ```
 
 {{% notice Tip %}}
-Setting *better* limits may not be desirable, as doing so may have a performance impact. To improve portability across devices and implementations, applications should generally only request better limits if they are required. 
+Setting *better* limits might not be preferable, as doing so might have a consequential performance impact. To improve portability across devices and implementations, applications should generally only request better limits if they are required. 
 
-It is recommended to read more about ["Supported Limits"](https://developer.mozilla.org/en-US/docs/Web/API/GPUSupportedLimits) and ["limits"](https://gpuweb.github.io/gpuweb/#limits).
+It is recommended that you to read more about ["Supported Limits"](https://developer.mozilla.org/en-US/docs/Web/API/GPUSupportedLimits) and ["limits"](https://gpuweb.github.io/gpuweb/#limits).
 {{% /notice %}}
 
 Use the `requestDevice()` API to request device:
@@ -181,7 +181,7 @@ static auto errorCallback = device.setUncapturedErrorCallback([](ErrorType type,
 ```
 
 {{% notice Tip %}}
-While creating a device, use a callback function `setUncapturedErrorCallback`, this helps in capturing validation and other errors with the WebGPU device. 
+While creating a device, use the callback function `setUncapturedErrorCallback`. This helps in capturing validation and other errors with the WebGPU device. 
 {{% /notice %}}
 
-Proceed to learn how to render 3D objects. 
\ No newline at end of file
+You can now move on to learn how to render 3D objects. 
\ No newline at end of file

From ceb2b73f2f33d2467d18e9015df14589b14825bf Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 3 Dec 2024 09:20:20 -0600
Subject: [PATCH 79/87] Remove draft from WPA Learning Path

---
 .../laptops-and-desktops/windowsperf_wpa_plugin/_index.md     | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 0f33fff372..1518a9cfce 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -1,10 +1,6 @@
 ---
 title: Get started with the Windows Performance Analyzer (WPA) plugin for WindowsPerf
 
-draft: true
-cascade:
-    draft: true
-
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for software developers interested in using the Windows Performance Analyzer (WPA) plugin for performance analysis.

From 4cef27cc11469b9feebe3f1431cb53d3fbb92d7b Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 16:37:01 +0000
Subject: [PATCH 80/87] Final checks.

---
 .../android_webgpu_dawn/1-webgpu-fundamentals.md            | 2 +-
 .../android_webgpu_dawn/3-integrate-dawn.md                 | 2 +-
 .../android_webgpu_dawn/4-using-webgpu-apis.md              | 6 +++---
 .../5-render-a-simple-3D-object-part-1.md                   | 4 ++--
 .../6-render-a-simple-3D-object-part-2.md                   | 6 +++---
 .../android_webgpu_dawn/7-Profiling-App-using-Streamline.md | 2 +-
 6 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
index c5cd651c18..4ffcff0a43 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/1-webgpu-fundamentals.md
@@ -28,7 +28,7 @@ The high-level view of WebGPU is shown below in Figure 1:
 
 ## What are the benefits of WebGPU?
 
-WebGPU takes into account lessons learned from earlier standards such as WebGL and OpenGL, and provides the following benefits: 
+WebGPU uses lessons learned from earlier standards such as WebGL and OpenGL to develop an improved offering, and provides the following benefits: 
 
 * A reasonable level of abstraction.
 * Improved performance.
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
index ca5be128bb..544058f2ac 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/3-integrate-dawn.md
@@ -37,7 +37,7 @@ GameActivity performs the following functions:
 * It renders into a SurfaceView, making it easier for games to interact with other UI components.
 
 {{% notice Tip %}}
-You can find more information about Android Game Activity and its capabilities in the [Game Activity documentation](https://developer.android.com/games/agdk/game-activity).
+You can find more information about Android Game Activity and its capabilities in the [Game Activity Documentation](https://developer.android.com/games/agdk/game-activity).
 {{% /notice %}}
 
 ## Download project source files
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
index 1a2ebaf79c..eec6f8f291 100755
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/4-using-webgpu-apis.md
@@ -56,9 +56,9 @@ The same host system might expose multiple adapters if it has access to multiple
 
 You can use these features and limits to determine the overall capabilities of the system before requesting the device. The adapter is used to access the capabilities of the user’s hardware, which are used to select the behavior of your application among different code paths. 
 
-Once a code path is chosen, a device is created with the chosen capabilities. Only the capabilities selected for this device are  allowed in the rest of the application. This way, it is notpossible to inadvertently rely on capabilities specific to a device.
+Once a code path is chosen, a device is created with the chosen capabilities. Only the capabilities selected for this device are  allowed in the rest of the application. This way, it is not possible to inadvertently rely on capabilities specific to a device.
 
-![Supported Limits #center](images/adapter_supported_limits.png "Figure 7: Adapter Supported Limit s")
+![Supported Limits #center](images/adapter_supported_limits.png "Figure 7: Adapter Supported Limits")
 
 {{% notice Tip %}}
 To use the adapter/device duality in an advanced way, you can set up multiple limit presets and select one depending on the adapter. 
@@ -157,7 +157,7 @@ requiredLimits.limits.minUniformBufferOffsetAlignment = supportedLimits.limits.m
 {{% notice Tip %}}
 Setting *better* limits might not be preferable, as doing so might have a consequential performance impact. To improve portability across devices and implementations, applications should generally only request better limits if they are required. 
 
-It is recommended that you to read more about ["Supported Limits"](https://developer.mozilla.org/en-US/docs/Web/API/GPUSupportedLimits) and ["limits"](https://gpuweb.github.io/gpuweb/#limits).
+It is recommended that you to read more about ["Supported Limits"](https://developer.mozilla.org/en-US/docs/Web/API/GPUSupportedLimits) and ["Limits"](https://gpuweb.github.io/gpuweb/#limits).
 {{% /notice %}}
 
 Use the `requestDevice()` API to request device:
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
index 67e7f82d17..cd8d77ac10 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/5-render-a-simple-3D-object-part-1.md
@@ -10,7 +10,7 @@ layout: learningpathall
 
 Graphic applications must operate with two processors, the CPU and the GPU. 
 
-These two processors run on different timelines. For optimal performance, commands intended for the GPU are batched and sent through a command queue. The GPU consumes this queue whenever it is ready, and in this way processors minimize the time spent idling for their sibling to respond. 
+These two processors run on different timelines. For optimal performance, commands intended for the GPU are batched and sent through a command queue. The GPU consumes this queue whenever it is ready, and in this way, processors minimize the time spent idling for their sibling to respond. 
 
 A WebGPU device has a single queue, which is used to send both commands and data. You can get it with `wgpuDeviceGetQueue()`.
 
@@ -50,7 +50,7 @@ The main things to notice in the above image are:
 To execute shaders on the GPU, you need to create all of these resources and set up this state. Creation of resources is relatively straightforward.
 
 {{% notice Note %}}
-Most WebGPU resources can not be changed after creation. You can change their contents but not their size, usage, and format. 
+Most WebGPU resources cannot be changed after creation. You can change their contents but not their size, usage, and format. 
 
 If you want to change something create a new resource and destroy the old one.
 {{% /notice %}}
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
index 54d6e1c638..8cd399a404 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/6-render-a-simple-3D-object-part-2.md
@@ -22,7 +22,7 @@ The steps are listed below:
 
 All these steps are common in graphics programming, and WebGPU allows you to perform these same operations. 
 
-It is a good idea to read the individual chapters in the [3D rendering](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/index.html) section to learn more.
+It is a good idea to read the individual chapters in the [3D Rendering](https://eliemichel.github.io/LearnWebGPU/basic-3d-rendering/index.html) section to learn more.
 
 ### Loading 3D objects
 
@@ -51,7 +51,7 @@ You are now ready to render a 3D object.
 
 You can run a rendering pass and *draw* something onto our *surface*. 
 
-To encode any commands to be issued to GPU, you need to create a `CommandEncoder`. Modern APIs record commands into command buffers,rather than issuing commands one by one, and submit all of them at once. 
+To encode any commands to be issued to GPU, you need to create a `CommandEncoder`. Modern APIs record commands into command buffers, rather than issuing commands one by one, and submit all of them at once. 
 
 In WebGPU, this is done through a `CommandEncoder` as shown below:
 
@@ -123,7 +123,7 @@ Make sure you release the created encoders and buffers by calling the respective
 {{% /notice %}}
 
 {{% notice Note %}}
-By default Dawn runs callbacks only when the device “ticks”, so the error callbacks are invoked in a different call stack than where the error occurred, making the breakpoint less informative. 
+By default, Dawn runs callbacks only when the device “ticks”, so the error callbacks are invoked in a different call stack than where the error occurred, making the breakpoint less informative. 
 
 To force Dawn to invoke error callbacks as soon as there is an error, you can enable an instance toggle:
 
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
index 5892932817..1f5326e38d 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/7-Profiling-App-using-Streamline.md
@@ -42,7 +42,7 @@ You might have to zoom into the data up to the maximum (**500 us**), as you are
 
 You can analyze two consecutive frames as shown below:
 
-!["Two consecutive frames" #center](./images/Streamline-mali-analysis.png "Figure 14: Two consecutive frames")
+!["Two Consecutive Frames" #center](./images/Streamline-mali-analysis.png "Figure 14: Two Consecutive Frames")
 
 Arm has worked with the Dawn team to optimize data uploading to GPU buffers for Mali GPUs. 
 

From d4efb3b6441727f6106977ce5eeafb6748b94c06 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Tue, 3 Dec 2024 17:11:12 +0000
Subject: [PATCH 81/87] Editorial Review.

---
 content/install-guides/git-woa.md | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/content/install-guides/git-woa.md b/content/install-guides/git-woa.md
index 14c4899d09..432f39e75b 100644
--- a/content/install-guides/git-woa.md
+++ b/content/install-guides/git-woa.md
@@ -47,27 +47,29 @@ Once you have downloaded Git, run the installer `.exe` file on a Windows on Arm
 
 The installer starts. 
 
-Click Next to acknowledge the GNU General Public License.
+Click **Next** to acknowledge the GNU General Public License.
 
-Set the destination location or accept the default location and click Next.
+Set the destination location or accept the default location, and click **Next**.
 
-Continue to click Next for any configuration settings. You can accept all defaults if you are unsure of specific settings.
+Continue to click **Next** for the configuration settings. You can accept all defaults if you are unsure of specific settings.
 
-At the end of the install process you see the screen below indicating setup has finished installing Git:
+At the end of the install process, you see the screen below indicating setup has finished installing Git:
 
 ![Install](/install-guides/_images/git-woa.png)
 
-Click the Finish button to complete installation. 
+Click the **Finish** button to complete installation. 
 
 ## How do I use Git on Windows? 
 
-You can use Git on Windows from a Command Prompt or using Git Bash, a Linux-like terminal experience which includes Git and many other Linux commands. 
+You can use Git on Windows from a Command Prompt or using Git Bash. 
+
+Git Bash is a Linux-like terminal experience which includes Git and many other Linux commands. 
 
 {{% notice Note %}}
 Git is not automatically added to your search path during installation. 
 {{% /notice %}}
 
-To use Git, click the Windows Start button and then click `All apps`.
+To use Git, click the Windows **Start** button and then click **All apps**.
 
 You see the Git folder in the G section.
 
@@ -77,12 +79,12 @@ There are menu items for multiple ways to start Git.
 
 ## How can I use Git in a Windows Command Prompt?
 
-Start a Git Command Prompt by selecting `Git CMD` from the start menu.
+Start a Git Command Prompt by selecting **Git CMD** from the **Start** menu.
 
 ![CMD](/install-guides/_images/git3-woa.png)
 
 
-To see the help message enter:
+To see the help message, enter:
 
 ```cmd
 git help
@@ -92,11 +94,11 @@ You can use Git from this Command Prompt.
 
 ## How can I use Git with Git Bash?
 
-To use Git in a Linux-like environment, select `Git Bash` from the start menu.
+To use Git in a Linux-like environment, select **Git Bash** from the start menu.
 
 ![CMD](/install-guides/_images/git4-woa.png)
 
-Click the colored icon on the top left corner of the Git Bash window and then click `Options` to change the appearance of the window, including colors, fonts, and font sizes. 
+Click the colored icon in the top-left corner of the Git Bash window, and then click **Options** to change the appearance of the window, including colors, fonts, and font sizes. 
 
 ![Options](/install-guides/_images/git5-woa.png)
 

From 25e06af623be905f2f1912a8d3c395eda2b87a62 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 3 Dec 2024 11:55:32 -0600
Subject: [PATCH 82/87] Remove direct link to review questions in embedded
 migration to Arm Learning Path.

---
 .../learning-paths/embedded-systems/migration/6_run_evaluate.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/embedded-systems/migration/6_run_evaluate.md b/content/learning-paths/embedded-systems/migration/6_run_evaluate.md
index 8a2cfe7754..256a3ea14c 100644
--- a/content/learning-paths/embedded-systems/migration/6_run_evaluate.md
+++ b/content/learning-paths/embedded-systems/migration/6_run_evaluate.md
@@ -45,4 +45,4 @@ Emulation does not give a representative view of how efficiently the algorithms
 
 You have now ported an `x86_64` application to `aarch64`, built and run the ported application on `aarch64` using emulation, well done!
 
-If you have access to Arm hardware, continue to the next section [Evaluating real hardware](/learning-paths/embedded-systems/migration/7_alternative). If you don't have access to Arm hardware you can jump straight to the [Review](/learning-paths/embedded-systems/migration/_review) and test your knowledge.
\ No newline at end of file
+Continue to the next section to evaluate application performance on Arm hardware.

From 7036ff094f0eba4adcbcaff57a2b8d8b69ed7937 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Tue, 3 Dec 2024 12:42:46 -0600
Subject: [PATCH 83/87] Update WSL Learning Path

---
 .../laptops-and-desktops/wsl2/setup.md           | 16 ++++++++++------
 .../laptops-and-desktops/wsl2/ssh.md             |  2 +-
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/laptops-and-desktops/wsl2/setup.md b/content/learning-paths/laptops-and-desktops/wsl2/setup.md
index 440f79da48..f1a96fb493 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/setup.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/setup.md
@@ -25,6 +25,7 @@ WSL 2 replaces the system call translation layer provided in WSL 1 with the late
 WSL 2 can run containers for application development. WSL 2 provides much faster file I/O compared to WSL 1.
 
 ## Install WSL 2
+
 Installing WSL 2 requires Windows 11. It is also possible to install WSL 2 on certain Windows 10 versions, but these instructions were tested on Windows 11. Windows 11 is recommended to complete all of the examples in this Learning Path. All of the examples have been tested using WSL 2. If only WSL is specified it means WSL 2.
 
 Here is the short version on how to install WSL 2. Microsoft documentation provides a [quickstart](https://docs.microsoft.com/en-us/windows/wsl/install-win10) with full details on how to install WSL 2. There are also numerous tutorials available (for non-Arm architectures).
@@ -73,12 +74,15 @@ wsl --list --online
 The output will list the available distributions:
 
 ```output
-NAME            FRIENDLY NAME
-Ubuntu          Ubuntu
-Debian          Debian GNU/Linux
-Ubuntu-18.04    Ubuntu 18.04 LTS
-Ubuntu-20.04    Ubuntu 20.04 LTS
-Ubuntu-22.04    Ubuntu 22.04 LTS
+NAME                   FRIENDLY NAME
+Ubuntu                 Ubuntu
+Debian                 Debian GNU/Linux
+kali-linux             Kali Linux Rolling
+Ubuntu-18.04           Ubuntu 18.04 LTS
+Ubuntu-20.04           Ubuntu 20.04 LTS
+Ubuntu-22.04           Ubuntu 22.04 LTS
+Ubuntu-24.04           Ubuntu 24.04 LTS
+openSUSE-Tumbleweed    openSUSE Tumbleweed
 ```
 
 Install a distribution from this list:
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/ssh.md b/content/learning-paths/laptops-and-desktops/wsl2/ssh.md
index 4621c441a4..e12a3e7983 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/ssh.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/ssh.md
@@ -22,7 +22,7 @@ Substitute your username and the filename to be copied.
 cp /mnt/c/Users/<username>/Downloads/<filename> .
 ```
 
-If SSH is needed to access WSL from a different machine continue with the instructions below:
+If SSH is needed to access WSL from a different machine continue with the instructions below.
 
 ## Install SSH server
 

From e92a2faf7ea10cd9f0c3d534ebddae8c85a19aa9 Mon Sep 17 00:00:00 2001
From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com>
Date: Thu, 5 Dec 2024 10:45:21 +0000
Subject: [PATCH 84/87] Added intermediate as level.

---
 .../smartphones-and-mobile/android_webgpu_dawn/_index.md        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index 3a05c5e9b9..afffc360ee 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -27,7 +27,7 @@ prerequisites:
 author_primary: Varun Chari, Albin Bernhardsson
 
 ### Tags
-skilllevels: Advanced
+skilllevels: Intermediate
 subjects: GPU
 armips:
     - Cortex-A

From c8382e4cd80117d40614fb1dab1badbb68c9c2e2 Mon Sep 17 00:00:00 2001
From: Jason Andrews <jason.r.andrews@comcast.net>
Date: Thu, 5 Dec 2024 07:20:40 -0600
Subject: [PATCH 85/87] spelling and category updates

---
 .wordlist.txt                                 | 47 ++++++++++++++++++-
 .../laptops-and-desktops/_index.md            | 11 +++--
 .../learning-paths/microcontrollers/_index.md |  9 ++--
 .../smartphones-and-mobile/_index.md          | 11 +++--
 .../android_webgpu_dawn/_index.md             |  4 +-
 .../profiling-ml-on-arm/_index.md             |  4 ++
 6 files changed, 69 insertions(+), 17 deletions(-)

diff --git a/.wordlist.txt b/.wordlist.txt
index 47fe254fa8..12f6888c86 100644
--- a/.wordlist.txt
+++ b/.wordlist.txt
@@ -3335,4 +3335,49 @@ webgpufundamentals
 wgpuQueueSubmit
 wgpuQueueWriteBuffer
 wgpuQueueWriteTexture
-wpa
\ No newline at end of file
+wpa
+CAMs
+CODENAME
+COMs
+Chaodong
+CreateWorkload
+Disqus
+ExecuteNetwork
+FPC
+HX
+Hejmadi
+Himax
+Kieran
+LHwilBhvNU
+LastWriteTime
+LiteRT
+OV
+Seeed
+WebGPU’s
+WiseEye
+Yolov
+blp
+dLayer
+dawnwebgpu
+dmesg
+fm
+himax
+il
+juXXgrDDaUdmi
+mins
+mobilenet
+msvc
+od
+ons
+scm
+seeedstudio
+sscript
+tflm
+tw
+vc
+videoio
+wiseeye
+wlcsp
+xB
+xmodem
+yolov
\ No newline at end of file
diff --git a/content/learning-paths/laptops-and-desktops/_index.md b/content/learning-paths/laptops-and-desktops/_index.md
index 7f6fe66d59..857b0f965b 100644
--- a/content/learning-paths/laptops-and-desktops/_index.md
+++ b/content/learning-paths/laptops-and-desktops/_index.md
@@ -13,11 +13,11 @@ operatingsystems_filter:
 - ChromeOS: 1
 - Linux: 29
 - macOS: 7
-- Windows: 36
+- Windows: 37
 subjects_filter:
 - CI-CD: 3
 - Containers and Virtualization: 6
-- Migration to Arm: 25
+- Migration to Arm: 26
 - Performance and Architecture: 20
 subtitle: Create and migrate apps for power efficient performance
 title: Laptops and Desktops
@@ -31,10 +31,10 @@ tools_software_languages_filter:
 - Automotive: 1
 - C: 2
 - C#: 5
-- C++: 2
+- C++: 3
 - C/C++: 4
 - CCA: 1
-- Clang: 9
+- Clang: 10
 - CMake: 2
 - Coding: 19
 - CSS: 1
@@ -56,6 +56,7 @@ tools_software_languages_filter:
 - Neon: 1
 - Neovim: 1
 - Node.js: 3
+- OpenCV: 1
 - perf: 2
 - Python: 2
 - Qt: 2
@@ -65,7 +66,7 @@ tools_software_languages_filter:
 - SVE: 1
 - SVE2: 1
 - Trusted Firmware: 1
-- Visual Studio: 9
+- Visual Studio: 10
 - Visual Studio Code: 9
 - VS Code: 2
 - Windows Forms: 1
diff --git a/content/learning-paths/microcontrollers/_index.md b/content/learning-paths/microcontrollers/_index.md
index f139834ead..caeda206bc 100644
--- a/content/learning-paths/microcontrollers/_index.md
+++ b/content/learning-paths/microcontrollers/_index.md
@@ -8,14 +8,14 @@ key_ip:
 maintopic: true
 operatingsystems_filter:
 - Baremetal: 25
-- Linux: 4
-- macOS: 2
+- Linux: 5
+- macOS: 3
 - RTOS: 10
 - Windows: 2
 subjects_filter:
 - CI-CD: 3
 - Libraries: 3
-- ML: 7
+- ML: 8
 - Performance and Architecture: 11
 - RTOS Fundamentals: 4
 - Security: 2
@@ -37,6 +37,7 @@ tools_software_languages_filter:
 - FVP: 1
 - GCC: 5
 - GitHub: 2
+- Himax SDK: 1
 - IP Explorer: 2
 - Keil: 7
 - Keil MDK: 3
@@ -44,7 +45,7 @@ tools_software_languages_filter:
 - MPS3: 1
 - Node.js: 1
 - Paddle: 1
-- Python: 1
+- Python: 2
 - PyTorch: 1
 - Raspberry Pi: 1
 - RTX: 2
diff --git a/content/learning-paths/smartphones-and-mobile/_index.md b/content/learning-paths/smartphones-and-mobile/_index.md
index 0461503058..3184b831e0 100644
--- a/content/learning-paths/smartphones-and-mobile/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/_index.md
@@ -10,14 +10,14 @@ key_ip:
 - Mali
 maintopic: true
 operatingsystems_filter:
-- Android: 23
-- Linux: 21
+- Android: 24
+- Linux: 22
 - macOS: 10
 - Windows: 10
 subjects_filter:
 - Gaming: 6
-- Graphics: 3
-- ML: 8
+- Graphics: 4
+- ML: 9
 - Performance and Architecture: 24
 subtitle: Optimize Android apps and build faster games using cutting-edge Arm tech
 title: Smartphones and Mobile
@@ -27,7 +27,7 @@ tools_software_languages_filter:
 - Android: 3
 - Android NDK: 1
 - Android SDK: 1
-- Android Studio: 7
+- Android Studio: 8
 - Arm Development Studio: 1
 - Arm Mobile Studio: 1
 - Arm Performance Studio: 2
@@ -61,6 +61,7 @@ tools_software_languages_filter:
 - Rust: 2
 - SDDiskTool: 1
 - SVE2: 1
+- tflite: 1
 - Total Compute: 1
 - Trusted Firmware: 1
 - Unity: 6
diff --git a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
index afffc360ee..5fa6f5e6c9 100644
--- a/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/android_webgpu_dawn/_index.md
@@ -27,8 +27,8 @@ prerequisites:
 author_primary: Varun Chari, Albin Bernhardsson
 
 ### Tags
-skilllevels: Intermediate
-subjects: GPU
+skilllevels: Advanced
+subjects: Graphics
 armips:
     - Cortex-A
 tools_software_languages:
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 1271520b0d..09b49edbdb 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -1,6 +1,10 @@
 ---
 title: Profile the performance of ML models on Arm
 
+draft: true
+cascade:
+    draft: true
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers who want to learn how to profile the performance of their ML models running on Arm devices.

From 107c01baa986a2e3584627686e13b6364d011829 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 5 Dec 2024 09:11:16 -0500
Subject: [PATCH 86/87] Update _index.md

---
 .../learning-paths/laptops-and-desktops/win-opencv/_index.md  | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 561cadd2f9..939f756cfa 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build OpenCV applications on Windows on Arm
-
+draft: true
+cascade:
+    draft: true
 minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for software developers who want to use OpenCV with Windows on Arm devices.

From 99972fc7429facbe90c796b1f42fa81fb6ba2a7b Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 5 Dec 2024 09:12:07 -0500
Subject: [PATCH 87/87] Update _index.md

---
 .../learning-paths/microcontrollers/yolo-on-himax/_index.md   | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
index f1c4abe72e..8882ef4e61 100644
--- a/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/microcontrollers/yolo-on-himax/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Run a Computer Vision Model on a Himax Microcontroller
-
+draft: true
+cascade:
+    draft: true
 minutes_to_complete: 90
 
 who_is_this_for: This is an introduction topic explaining how to run a computer vision application on an embedded device from Himax. The example uses an off-the-shelf Himax WiseEye2 module which is based on Arm Cortex-M55 and Ethos-U55.