Add dc docs

docs/psoc6/integrations/deepcraft_integration.rst

@@ -0,0 +1,70 @@
+.. _psoc6_mpy_deepcraft_integration:
+
+MicroPython - DEEPCRAFT™ Integration 
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`DEEPCRAFT™ <https://www.infineon.com/cms/en/design-support/software/deepcraft-edge-ai-solutions/>`_ is Infineon's comprehensive new Edge AI software & tools offering designed to fastrack edge machine learning 
+application development.
+
+`DEEPCRAFT™ Studio`_ is a development platform for AI on edge devices. It provides unique modeling capabilities 
+to make custom edge AI models for PSOC-based hardware. Its integration with MicroPython allows an easy end-to-end 
+application development and deployment journey. 
+
+Follow along to build your first edge Machine Learning (ML) application with MicroPython and DEEPCRAFT™ Studio. 🚀
+
+Overview
+=========
+This solution follows a standard edge ML application workflow, consisting of two primary phases: training and inferencing. 
+The training phase is efficiently managed within DEEPCRAFT™ Studio.
+
+The diagrams below provide a detailed overview of the workflows for both phases, highlighting the tools and steps involved, 
+from data acquisition to model deployment.
+
+.. image:: img/training_phase.png
+        :width: 1000
+
+During the training phase, the edge device executes a MicroPython script that streams raw sensor data to a host machine. A capture server running on the host 
+receives this data and stores it as timestamped files. These files are then imported into DEEPCRAFT™ Studio, where they undergo labeling, preprocessing, 
+and are used to train a machine learning model tailored to the specific application. With MicroPython integration, the trained model can be effortlessly converted 
+into a runtime-loadable format and deployed to the device's filesystem. This streamlined workflow, which supports iterative tuning for optimal accuracy, 
+greatly simplifies the model conversion and deployment process.
+
+
+.. image:: img/inferencing_phase.png
+        :width: 1000
+
+After achieving satisfactory accuracy, the model is ready for deployment in the final application. During this phase, a MicroPython script stored on the edge device's filesystem 
+manages sensor data acquisition and feeds it to the dynamically loaded model. The pre-trained model processes the input data and generates class probabilities based on the learned patterns. 
+These outputs can then be utilized to trigger actions through peripherals (e.g., LEDs, buzzers) or transmitted to external interfaces, such as a web dashboard, for visualization.
+
+With this streamlined development workflow, let's proceed to build a complete edge ML application step by step.
+
+Pre-requisites
+================
+
+Ensure the following tools are installed on your system:
+
+1. `DEEPCRAFT™ Studio`_
+2. `Capture Server cloned in your local <https://bitbucket.org/imagimob/captureserver/src/master/>`_
+
+Supported Boards
+==================
+- `CY8CKIT-062S2-AI <https://www.infineon.com/cms/en/product/evaluation-boards/cy8ckit-062s2-ai/>`_
+
+1. Data Acquisition
+======================
+
+This part is covered in the `data acquisition repository <https://github.com/Infineon/deepcraft-micropython-data-acquisition>`_, which provides 
+instructions and examples for data acquisition using MicroPython.
+
+2. Model Deployment
+=====================
+
+This part is covered in the `model converter repository <https://github.com/Infineon/deepcraft-micropython-converter>`_, which provides instructions 
+on how to convert a DEEPCRAFT™ model into a MicroPython-compatible format and deploy it on the PSOC board.
+
+Example Projects
+==================
+- `Edge AI-based baby cry detector with Home Assistant integration <https://www.hackster.io/Infineon_Team/ai-baby-cry-detector-with-home-assistant-integration-05576f>`_
+
+.. _DEEPCRAFT™ Studio: https://softwaretools.infineon.com/tools/com.ifx.tb.tool.deepcraftstudio

docs/psoc6/quickref.rst

@@ -21,6 +21,16 @@ working with this port it may be useful to get an overview of the microcontrolle
    installation.rst
    mpy-usage.rst
 
+And if you are already familiar with MicroPython enablement for PSoC6 and want to try it's integrations
+in other application domains, check below:
+
+.. toctree::
+   :maxdepth: 1
+   :includehidden:
+
+   integrations/deepcraft_integration.rst
+
+
 .. note::
 
     The PSoC6™ port is now a mature port and is expected any MicroPython built-in
@@ -1006,4 +1016,5 @@ The NeoPixel driver can be used as follows (see the :mod:`neopixel` for more det
 .. note::
    - The timing parameter can be used in the `NeoPixel()` constructor with timing tuples supported by the `machine.bitstream()` module. The timing parameter is optional and by default set to 1 which is the default timing [400, 850, 800, 450] for WS2812B LEDs at 800kHz.
    - Use timing = 0 for WS2812B LEDs at 400kHz ie, [800, 1700, 1600, 900].
-   - Use timing = [300, 900, 600, 600] for SK6812 LEDs. 
+   - Use timing = [300, 900, 600, 600] for SK6812 LEDs. 
+