Update README for MediaTek backend

backends/mediatek/scripts/README.md

@@ -10,41 +10,60 @@ Before you begin, ensure you have the following prerequisites installed and conf
 
 - **Download Buck2**: Obtain Buck2 from the official [releases page](https://github.com/facebook/buck2/releases/tag/2024-02-01).
 - **Add to PATH**: Extract the downloaded file and add the directory to your system's `$PATH` environment variable.
-```bash
-export PATH=<path_to_buck>:$PATH
-```
+   ```bash
+   export PATH=<path_to_buck>:$PATH
+   ```
 
 ### 2. Android NDK
 
 - **Download Android NDK**: Acquire the Android NDK from the [Android developer site](https://developer.android.com/ndk/downloads).
 - **Set NDK Path**: Ensure that the `$ANDROID_NDK` environment variable is set to the path where the NDK is located.
-```bash
-export ANDROID_NDK=<path_to_android_ndk>
-```
+   ```bash
+   export ANDROID_NDK=<path_to_android_ndk>
+   ```
 
 ### 3. MediaTek ExercuTorch Libraries
 
-Download the following libraries from MediaTek's NeuroPilot portal (link to be added):
+Download [NeuroPilot Express SDK](https://neuropilot.mediatek.com/resources/public/npexpress/en/docs/npexpress) from MediaTek's NeuroPilot portal:
 
 - `libneuronusdk_adapter.mtk.so`: This universal SDK contains the implementation required for executing target-dependent code on the MediaTek chip.
 - `libneuron_buffer_allocator.so`: This utility library is designed for allocating DMA buffers necessary for model inference.
-```bash
-export NEURON_BUFFER_ALLOCATOR_LIB=<path_to_buffer_allocator>
-```
+- `mtk_converter-8.8.0.dev20240723+public.d1467db9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl`: This library preprocess the model into a MediaTek representation.
+- `mtk_neuron-8.2.2-py3-none-linux_x86_64.whl`: This library converts the model to binaries.
 
 ## Setup
 
-Follow the steps below to set up your build environment:
+Follow the steps below to setup your build environment:
+
+1. **Setup ExercuTorch Environment**: Refer to the [Setting up ExercuTorch](https://pytorch.org/executorch/stable/getting-started-setup) guide for detailed instructions on setting up the ExercuTorch environment.
+
+2. **Setup MediaTek Backend Environment**
+- Install the dependent libs. Ensure that you are inside backends/mediatek/ directory
+   ```bash
+   pip3 install -r requirements.txt
+   ```
+- Install the two .whl downloaded from NeuroPilot Portal
+   ```bash
+   pip3 install mtk_neuron-8.2.2-py3-none-linux_x86_64.whl
+   pip3 install mtk_converter-8.8.0.dev20240723+public.d1467db9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+   ```
+- Set evironment variables for building backend
+   ```bash
+   export NEURON_BUFFER_ALLOCATOR_LIB=<path_to_buffer_allocator>
+   ```
 
-1. **ExercuTorch Official Tutorial**: Refer to the [Setting up ExercuTorch](https://pytorch.org/executorch/stable/getting-started-setup) guide for detailed instructions on setting up the ExercuTorch environment.
+## Build
 
-2. **Build Script**: Once the prerequisites are in place, run the `mtk_build.sh` script to start the build process.
+1. **Build MediaTek Backend**: Once the prerequisites are in place, run the `mtk_build.sh` script to start the build process, MediaTek backend will be built under `cmake-android-out/backends/` as `libneuron_backend.so`
 
    ```bash
    ./mtk_build.sh
    ```
-3. **Push MediaTek universal SDK to the device**: push libneuronusdk_adapter.mtk.so to the phone and export it to the `$LD_LIBRARY_PATH` environment variable before executing ExercuTorch with MediaTek backend.
+
+## Run
+
+1. **Push MediaTek universal SDK and MediaTek backend to the device**: push `libneuronusdk_adapter.mtk.so` and `libneuron_backend.so` to the phone and export it to the `$LD_LIBRARY_PATH` environment variable before executing ExercuTorch with MediaTek backend.
 
    ```bash
-   export LD_LIBRARY_PATH=<path_to_usdk>:$LD_LIBRARY_PATH
+   export LD_LIBRARY_PATH=<path_to_usdk>:<path_to_neuron_backend>:$LD_LIBRARY_PATH
    ```

examples/mediatek/README.md

@@ -23,30 +23,18 @@ examples/mediatek
 ├── mtk_build_examples.sh             # Script for building MediaTek backend and the examples
 └── README.md                         # Documentation for the examples (this file)
 ```
-# Examples
-## Build MediaTek examples
-1. Set up the environment by folllowing the instructions in `backends/mediatek/scripts`
-2. Build the backend and the examples by exedcuting the script:
-```bash
-./mtk_build_examples.sh
-```
+# Examples Build Instructions
 
-# AoT
 ## Environment Setup
-1. Setup ET Environment
-- Follow the instructions found in: https://pytorch.org/executorch/stable/getting-started-setup.html
-2. Setup MTK AoT Environment
-```bash
-// Ensure that you are inside executorch/examples/mediatek directory
-pip3 install -r requirements.txt
+- Follow the instructions of **Prerequisites** and **Setup** in `backends/mediatek/scripts/README.md`.
 
-// Download the two whl files from NeuroPilot Portal
-pip3 install mtk_neuron-8.2.2-py3-none-linux_x86_64.whl
-pip3 install mtk_converter-8.8.0.dev20240723+public.d1467db9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+## Build MediaTek Examples
+1. Build the backend and the examples by exedcuting the script:
+```bash
+./mtk_build_examples.sh
 ```
 
-## AoT Flow
-### llama
+## LLaMa Example Instructions
 ##### Note: Verify that localhost connection is available before running AoT Flow
 1. Exporting Models to `.pte`
 - In the `examples/mediatek directory`, run: