Merge branch 'main' of https://github.com/odincodeshen/arm-learning-paths

Author: odincodeshen

.github/workflows/deploy.yml

@@ -26,7 +26,7 @@ env:
 jobs:
   build_and_deploy:
     # The type of runner that the job will run on
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04-arm
     permissions:
       id-token: write
       contents: read
@@ -59,7 +59,7 @@ jobs:
         run: |
           hugo --minify
           cp learn-image-sitemap.xml public/learn-image-sitemap.xml
-          bin/pagefind --site "public"
+          bin/pagefind.aarch64 --site "public"
         env:
           HUGO_LLM_API: ${{ secrets.HUGO_LLM_API }}
           HUGO_RAG_API: ${{ secrets.HUGO_RAG_API }}

.wordlist.txt

@@ -3558,4 +3558,17 @@ threadCount
 threadNum
 useAPL
 vvenc
-workspaces
+workspaces
+ETDump
+ETRecord
+FAISS
+IVI
+PDFs
+Powertrain
+SpinTheCubeInGDI
+TaaS
+cloudsdk
+highcpu
+proj
+sln
+uploader

content/install-guides/cmake.md

@@ -34,7 +34,7 @@ This article provides quick instructions to install CMake for Arm Linux distribu
 
 ### How do I download and install CMake for Windows on Arm?
 
-Confirm you are using a Windows on Arm device such as Windows Dev Kit 2023 or a laptop such as Lenovo ThinkPad X13s or Surface Pro 9 with 5G.
+Confirm you are using a Windows on Arm device such as the Lenovo ThinkPad X13s or Surface Pro 9 with 5G.
 
 ### How do I download and install CMake for Arm Linux distributions?
 

content/install-guides/windows-perf-vs-extension.md

@@ -41,13 +41,13 @@ The WindowsPerf GUI extension is composed of several key features, each designed
 - **Output Logging**: All commands executed through the GUI are logged, ensuring transparency and supporting performance analysis.
 - **Sampling UI**: Customize your sampling experience by selecting events, setting frequency and duration, choosing programs for sampling, and comprehensively analyzing results. See screenshot below.
 
-![Sampling preview #center](../_images/wperf-vs-extension-sampling-preview.png "Sampling settings UI Overview")
+![Sampling preview #center](/install_guides/_images/wperf-vs-extension-sampling-preview.png "Sampling settings UI Overview")
 
 
 - **Counting Settings UI**: Build a `wperf stat` command from scratch using the configuration interface, then view the output in the IDE or open it with Windows Performance Analyzer (WPA). See screenshot below.
 
 
-![Counting preview #center](../_images/wperf-vs-extension-counting-preview.png "Counting settings UI Overview")
+![Counting preview #center](/install_guides/_images/wperf-vs-extension-counting-preview.png "Counting settings UI Overview")
 
 ## Before you begin
 
@@ -69,7 +69,7 @@ To install the WindowsPerf Visual Studio Extension from Visual Studio:
 4. Click on the search bar (Ctrl+L) and type `WindowsPerf`.
 5. Click on the **Install** button and restart Visual Studio.
 
-![WindowsPerf install page #center](../_images/wperf-vs-extension-install-page.png)
+![WindowsPerf install page #center](/install_guides/_images/wperf-vs-extension-install-page.png)
 
 ### Installation from GitHub
 

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md

@@ -6,12 +6,12 @@ weight: 2
 layout: learningpathall
 ---
 
-This Learning Path is about TinyML. It serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. You will learn how to setup on your host machine and target device to facilitate compilation and ensure smooth integration across all devices.
+This Learning Path is about TinyML. It serves as a starting point for learning how cutting-edge AI technologies may be used on even the smallest devices, making Edge AI more accessible and efficient. You will learn how to set up your host machine and target device to facilitate compilation and ensure smooth integration across devices.
 
 In this section, you get an overview of the domain with real-life use-cases and available devices.
 
 ## Overview
-TinyML represents a significant shift in machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
+TinyML represents a significant shift in machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
 
 ### Benefits and applications
 
@@ -42,7 +42,7 @@ TinyML is being deployed across multiple industries, enhancing everyday experien
 
 ### Examples of Arm-based devices
 
-There are many Arm-based off-the-shelf devices you can use for TinyML projects. Some of them are listed below, but the list is not exhaustive.
+There are many Arm-based devices you can use for TinyML projects. Some of them are listed below, but the list is not exhaustive.
 
 #### Raspberry Pi 4 and 5
 
@@ -64,6 +64,6 @@ The Arduino Nano, equipped with a suite of sensors, supports TinyML and is ideal
 
 In addition to hardware, there are software platforms that can help you build TinyML applications.
 
-Edge Impulse platform offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards.
+Edge Impulse offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards.
 
 Now that you have an overview of the subject, move on to the next section where you will set up an environment on your host machine.

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md

@@ -14,13 +14,13 @@ learning_objectives:
     - Understand the benefits of deploying AI models on Arm-based edge devices.
     - Select Arm-based devices for TinyML.
     - Install and configure a TinyML development environment.
-    - Perform best practices for ensuring optimal performance on constrained edge devices.
+    - Apply best practices for ensuring optimal performance on constrained edge devices.
 
 
 prerequisites:
     - Basic knowledge of machine learning concepts.
     - A Linux host machine or VM running Ubuntu 22.04 or higher.
-    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) **or** an Arm license to run the Corstone-300 Fixed Virtual Platform (FVP).
+    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) or an Arm license to run the Corstone-300 Fixed Virtual Platform (FVP).
 
 
 author_primary: Dominica Abena O. Amanfo

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_next-steps.md

@@ -9,6 +9,14 @@ further_reading:
         title: TinyML Brings AI to Smallest Arm Devices 
         link: https://newsroom.arm.com/blog/tinyml
         type: blog
+    - resource:
+        title: Arm Compiler for Embedded
+        link: https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded
+        type: documentation
+    - resource:
+        title: Arm GNU Toolchain
+        link: https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain
+        type: documentation
 
 
 

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md

@@ -1,15 +1,14 @@
 ---
 # User change
-title: "Build a Simple PyTorch Model"
+title: "Build a simple PyTorch model"
 
 weight: 7 # 1 is first, 2 is second, etc.
 
 # Do not modify these elements
 layout: "learningpathall"
 ---
 
-TODO connect this part with the FVP/board?
-With our environment ready, you can create a simple program to test the setup.
+With the development environment ready, you can create a simple PyTorch model to test the setup.
 
 This example defines a small feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between.
 
@@ -62,7 +61,7 @@ print("Model successfully exported to simple_nn.pte")
 
 Run the model from the Linux command line:
 
-```console
+```bash
 python3 simple_nn.py
 ```
 
@@ -76,15 +75,15 @@ The model is saved as a .pte file, which is the format used by ExecuTorch for de
 
 Run the ExecuTorch version, first build the executable:
 
-```console
+```bash
 # Clean and configure the build system
 (rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake ..)
 
 # Build the executor_runner target
 cmake --build cmake-out --target executor_runner -j$(nproc)
 ```
 
-You see the build output and it ends with:
+You will see the build output and it ends with:
 
 ```output
 [100%] Linking CXX executable executor_runner
@@ -93,7 +92,7 @@ You see the build output and it ends with:
 
 When the build is complete, run the executor_runner with the model as an argument:
 
-```console
+```bash
 ./cmake-out/executor_runner --model_path simple_nn.pte
 ```
 
@@ -112,3 +111,30 @@ Output 0: tensor(sizes=[1, 2], [-0.105369, -0.178723])
 
 When the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes.
 
+
+
+TODO: Debug issues when running the model on the FVP, kindly ignore anything below this 
+## Running the model on the Corstone-300 FVP 
+
+
+Run the model using: 
+
+```bash
+FVP_Corstone_SSE-300_Ethos-U55 -a simple_nn.pte -C mps3_board.visualisation.disable-visualisation=1
+```
+
+{{% notice Note %}}
+
+-C mps3_board.visualisation.disable-visualisation=1 disables the FVP GUI. This can speed up launch time for the FVP.
+
+The FVP can be terminated with Ctrl+C.
+{{% /notice %}}
+
+
+
+```output
+ 
+```
+
+
+You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Network.

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md

@@ -8,7 +8,7 @@ weight: 3
 layout: "learningpathall"
 ---
 
-In this section, you will prepare a development environment to compile the model. These instructions have been tested on Ubuntu 22.04, 24.04 and on Windows Subsystem for Linux (WSL).
+In this section, you will prepare a development environment to compile a machine learning model. These instructions have been tested on Ubuntu 22.04, 24.04 and on Windows Subsystem for Linux (WSL).
 
 ## Install dependencies
 
@@ -27,7 +27,7 @@ Create a Python virtual environment using `python venv`.
 python3 -m venv $HOME/executorch-venv
 source $HOME/executorch-venv/bin/activate
 ```
-The prompt of your terminal now has (executorch) as a prefix to indicate the virtual environment is active.
+The prompt of your terminal now has `(executorch)` as a prefix to indicate the virtual environment is active.
 
 
 ## Install Executorch
@@ -40,11 +40,11 @@ git clone https://github.com/pytorch/executorch.git
 cd executorch
 ```
 
-Run a few commands to set up the ExecuTorch internal dependencies.
+Run the commands below to set up the ExecuTorch internal dependencies.
+
 ```bash
 git submodule sync
 git submodule update --init
-
 ./install_requirements.sh
 ```
 
@@ -59,6 +59,8 @@ pkill -f buck
 
 ## Next Steps
 
-If you don't have the Grove AI vision board, use the Corstone-300 FVP proceed to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/)
+Your next steps depends on the hardware you have. 
+
+If you have the Grove Vision AI Module proceed to [Set up the Grove Vision AI Module V2](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/).
 
-If you have the Grove board proceed o to [Setup on Grove - Vision AI Module V2](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/)
+If you don't have the Grove Vision AI Module, you can use the Corstone-300 FVP instead, proceed to [Set up the Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/).

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md

@@ -10,22 +10,26 @@ layout: "learningpathall"
 
 ## Corstone-300 FVP Setup for ExecuTorch
 
-Navigate to the Arm examples directory in the ExecuTorch repository.
+Navigate to the Arm examples directory in the ExecuTorch repository and configure the Fixed Virtual Platform (FVP).
+
 ```bash
 cd $HOME/executorch/examples/arm
 ./setup.sh --i-agree-to-the-contained-eula
 ```
 
+Set the environment variables for the FVP.
+
 ```bash
 export FVP_PATH=${pwd}/ethos-u-scratch/FVP-corstone300/models/Linux64_GCC-9.3
 export PATH=$FVP_PATH:$PATH
 ```
-Test that the setup was successful by running the `run.sh` script.
+
+Confirm the installation was successful by running the `run.sh` script.
 
 ```bash
 ./run.sh
 ```
 
-TODO connect this part to simple_nn.py part?
+You will see a number of examples run on the FVP. 
 
-You will see a number of examples run on the FVP. This means you can proceed to the next section to test your environment setup.
+This confirms the installation, and you can proceed to the next section [Build a Simple PyTorch Model](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/build-model-8/).