Merge branch 'ArmDeveloperEcosystem:main' into eksctl

anupras-mohapatra-arm · web-flow · commit 65c83a1b7fc8 · 2026-04-15T14:02:54.000-05:00
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Prepare Docker image for Arm embedded development
 
+description: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded and Fixed Virtual Platforms, and test the containerized Arm development environment.
+
 minutes_to_complete: 30   
 
 who_is_this_for: This is an introductory topic for embedded software developers new to Docker.
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Learn how to run AI on Edge devices using Arduino Nano RP2040
 
+description: Learn how to collect and preprocess audio data using Edge Impulse, train an audio classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
+
 minutes_to_complete: 90
 
 who_is_this_for: This Learning Path is for beginners in Edge AI and TinyML, including developers, engineers, hobbyists, AI/ML enthusiasts, and researchers working with embedded AI and IoT.
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/program-and-deployment.md b/content/learning-paths/embedded-and-microcontrollers/edge/program-and-deployment.md
@@ -345,17 +345,17 @@ These messages indicate that your model is working and processing voice input as
 ## Record your voice to toggle the LED
 
 - Wait for the **Recording...** message to appear on the Serial Monitor. This means the system is ready to capture voice input.  
-- Say your command (for example, "on" or "off") clearly and promptly. The system records for only about one second.  
+- Say your command (for example, "on" or "off") when prompted. The system records for only about one second.  
 - The model will make a prediction based on your voice input and toggle the LED accordingly.  
-- You can adjust the **threshold** values in the code to control how confident the prediction must be before the LED toggles. This helps fine-tune the system’s responsiveness.  
+- You can adjust the **threshold** values in the code to control how confident the prediction must be before the LED toggles. This helps fine-tune the system's responsiveness.  
 - If the LED turns on when you say "on" and turns off when you say "off", your system is working correctly.
 
 
 ## Serial Monitor output
 
 Your Serial Monitor should look like the image below:
 
-![example image alt-text#center](images/serial_monitor.png "Circuit Connection")
+![Arduino IDE Serial Monitor output showing inference results with Recording message, label predictions (noise, off, on), and corresponding confidence scores for each voice command#center](images/serial_monitor.png "Serial Monitor showing voice command inference results")
 
 Congratulations, you’ve successfully programmed your first TinyML microcontroller! You've also built a functional, smart system to control an LED with your voice.
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/software-edge-impulse.md b/content/learning-paths/embedded-and-microcontrollers/edge/software-edge-impulse.md
@@ -48,12 +48,12 @@ In the following sections, you'll walk through each key page on the Edge Impulse
 
 
 
-![Screenshot of the Edge Impulse home page showing the main navigation and project dashboard alt-text#center](images/1.webp "Home page of Edge Impulse website")
+![Edge Impulse home page interface showing the main navigation menu at the top with Get Started, Platform, Resources, and Pricing options, and the central project dashboard area#center](images/1.webp "Home page of Edge Impulse website")
 
 
 ## Create a new project
 
-After you create your account and log in, the first step is to create a new project. Give your project a name that clearly reflects its purpose. This helps with easy identification, especially if you plan to build multiple models.
+After you create your account and log in, the first step is to create a new project. Give your project a name that reflects its purpose. This helps with easy identification, especially if you plan to build multiple models.
 
 For example, if you're building a keyword-spotting model, you might name it `Wake word detection`.
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/noncameracustomcomponent.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/noncameracustomcomponent.md
@@ -32,7 +32,7 @@ Within the 1.0.0 directory in S3, upload these files from your cloned repo (loca
 		models.tar.gz
 		samples.tar.gz
 
-Next, we need to edit the EdgeImpulseRunnerRuntimeInstallerComponent.yaml and change the artifact location from "YOUR\_S3\_ARTIFACT\_BUCKET" to the actual name of your S3 bucket name (you'll see "YOUR\_S3\_ARTIFACT\_BUCKET" near the bottom of the yaml file). Save the file. 
+Next, edit the EdgeImpulseRunnerRuntimeInstallerComponent.yaml and change the artifact location from "YOUR\_S3\_ARTIFACT\_BUCKET" to the actual name of your S3 bucket name (you'll see "YOUR\_S3\_ARTIFACT\_BUCKET" near the bottom of the yaml file). Save the file. 
 
 ### 3. Create the custom component
 
@@ -48,6 +48,6 @@ Finally, press "Create Component" and you should now have 2 custom components re
 
 ![CreateComponent](./images/GG_Create_NC_Component_3.png)
 
-Awesome!  Now that the non-camera support component is created, we can go back and continue with the deployment of these components to your edge device via the AWS IoT Greengrass deployment mechanism. Press "Return to Deployment Steps" below and continue!
+Now that the non-camera support component is created, return to the deployment steps and continue with deploying these components to your edge device via the AWS IoT Greengrass deployment mechanism. Select "Return to Deployment Steps" below to continue.
 
 ### [Return to Deployment Steps](/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/customcomponentdeployment/)
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Get started with Microcontrollers
 
+description: Learn where Arm architecture is used in microcontrollers and discover microcontroller hardware options for software development on Arm Cortex-M processors.
+
 minutes_to_complete: 10
 
 who_is_this_for: This is an introductory topic for software developers working on microcontroller applications and new to the Arm architecture.
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Introduction to TinyML on Arm using PyTorch and ExecuTorch
 
+description: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual Platform.
+
 minutes_to_complete: 40
 
 who_is_this_for: This is an introductory topic for developers and data scientists new to Tiny Machine Learning (TinyML) who want to explore its potential using PyTorch and ExecuTorch.
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build and run Arm Total Solutions for IoT
 
+description: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual hardware to understand complete IoT software stack construction.
+
 minutes_to_complete: 30   
 
 who_is_this_for: This is an introductory topic for embedded software developers interested in learning how a complete IoT software stack is constructed.
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Get started with object detection using a Jetson Orin Nano
 
+description: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time object detection from live video and image files using DetectNet and TensorRT.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for developers interested in integrating object detection into their applications.
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -1,5 +1,7 @@
 ---
 title: Use Linux on the NXP FRDM i.MX 93 board
+
+description: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network.
     
 minutes_to_complete: 120
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Debug Trusted Firmware-A and the Linux kernel on Arm FVP with Arm Development Studio 
 
+description: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then debug Trusted Firmware-A and the Linux kernel using Arm Development Studio.
+
 minutes_to_complete: 60
 
 who_is_this_for: This topic is for developers who want to run Linux on Arm Fixed Virtual Platforms (FVPs) and debug both Trusted Firmware-A and the Linux kernel using Arm Development Studio.
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Run a local LLM chatbot on a Raspberry Pi 5 
 
+description: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for anyone interested in running a local Large Language Model on a Raspberry Pi 5.
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/3_porting_analysis.md b/content/learning-paths/embedded-and-microcontrollers/migration/3_porting_analysis.md
@@ -62,7 +62,7 @@ p2 = _mm_loadu_si128((__m128i *)(inputPointer + i * width + j + 1));
 p3 = _mm_loadu_si128((__m128i *)(inputPointer + i * width + j + 2));
 ```
 
-The lines of code above is just a snippet from the function `SobelSimd` which has intrinsics prefixed with `_mm_`. These aren't supported on `aarch64` and will need to be ported for the application to compile on `aarch64`.
+The lines of code above are a snippet from the function `SobelSimd` which has intrinsics prefixed with `_mm_`. These aren't supported on `aarch64` and will need to be ported for the application to compile on `aarch64`.
 
 The table below summarizes the migration analysis.
 | | version | available on Arm | Comment |
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/4_development_environment.md b/content/learning-paths/embedded-and-microcontrollers/migration/4_development_environment.md
@@ -10,7 +10,7 @@ layout: "learningpathall"
 
 # Development environment
 
-The original application uses GNU Compiler Collection ([GCC](https://gcc.gnu.org/)) so you want to create a development environment with this compiler and with the same version (when possible). To migrate the application to `aarch64`, you can use the same `x86_64` machine you used to run the application. You can use an `aarch64` container for our development environment which allows you to compile and run the ported application on the `x86_64` machine.
+The original application uses GNU Compiler Collection ([GCC](https://gcc.gnu.org/)) so you want to create a development environment with this compiler and with the same version (when possible). To migrate the application to `aarch64`, you can use the same `x86_64` machine you used to run the application. You can use an `aarch64` container for your development environment which allows you to compile and run the ported application on the `x86_64` machine.
 
 See [Docker Engine](/install-guides/docker/docker-engine/) for instructions how to install Docker in your Linux environment.
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/7_alternative.md b/content/learning-paths/embedded-and-microcontrollers/migration/7_alternative.md
@@ -49,7 +49,7 @@ Install [Docker Engine](/install-guides/docker/docker-engine/) on the EC2 instan
 
 ### Raspberry Pi 4
 
-The Raspberry Pi is setup just like a normal desktop computer and the following is assumed:
+The Raspberry Pi is set up like a normal desktop computer and the following is assumed:
 * Running Raspberry Pi OS (64-bit)
 * Docker is installed
 * MicroSD card inserted
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Migrating x86_64 workloads to aarch64
 
+description: Learn the software migration methodology for porting Linux workloads from x86_64 to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications in containers.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an advanced topic for embedded software developers looking at migrating Linux workloads to aarch64.
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build and run the Arm Machine Learning Evaluation Kit examples
 
+description: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for embedded software developers interested in machine learning applications.
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Navigate Machine Learning development with Ethos-U processors
 
+description: Learn how to understand and select physical and virtual hardware targets for ML application development with Cortex-M and Ethos-U, identify software tools, and find example applications.
+
 armips:
 - Cortex-M
 - Ethos-U
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/sw.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/sw.md
@@ -106,7 +106,7 @@ The [Micro speech example for TensorFlow Lite](https://github.com/ARM-software/A
  
 Refer to the information included in the [AVH documentation](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html) for more details. 
 
-You can run micro speech is just a few steps on the AVH AMI:
+You can run micro speech in a few steps on the AVH AMI:
 
 ```console
 git clone https://github.com/ARM-software/AVH-TFLmicrospeech.git
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Add new debug targets to Arm Development Studio
 
+description: Learn how to create debug configurations for virtual platforms and development boards in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
+
 minutes_to_complete: 30   
 
 who_is_this_for: This is an introductory topic for embedded software developers new to Arm Development Studio.
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/dstream.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/dstream.md
@@ -62,7 +62,7 @@ Ensure `DSTREAM family` is selected from the Target Connection pulldown, and Bro
 
 To load an image, navigate to the `Files` tab, and browse to the appropriate ELF image. Then, in the `Debugger` tab, select `Debug from entry point`.
 
-If the target is already running with an image you can also just the debug symbols from the `Files` tab. In that scenario, in the `Debugger` tab, select `Connect Only`.
+If the target is already running with an image you can also load the debug symbols from the `Files` tab. In that scenario, in the `Debugger` tab, select `Connect Only`.
 
 Click `Debug` to connect to the platform, and commence your debug session.
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Deploy ExecuTorch firmware on NXP FRDM i.MX 93 for Ethos-U65 acceleration
 
+description: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU acceleration.
+
 minutes_to_complete: 120
 
 who_is_this_for: This is an introductory topic for developers and data scientists new to TinyML who want to observe ExecuTorch performance on a physical device.
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Migrating CMSIS-Packs to CMSIS v6
 
+description: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6 and update example projects for compatibility with the new CMSIS version.
+
 minutes_to_complete: 10
 
 who_is_this_for: This is an advanced topic for maintainers of CMSIS-Packs with device support.
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Migrating Projects to CMSIS v6
 
+description: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains, installing required CMSIS-Packs, and selecting the necessary software components.
+
 minutes_to_complete: 10
 
 who_is_this_for: This is an advanced topic for embedded developers who want to migrate their projects to CMSIS v6.
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build a Privacy-First LLM Smart Home on Raspberry Pi 5
 
+description: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama, control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without cloud services.
+
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for edge AI developers, Raspberry Pi hobbyists, and software engineers who want to build privacy-first smart home assistants. You’ll learn how to run large language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected devices, and deploy a web-based assistant without relying on cloud services.
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Create a ChatGPT voice bot on a Raspberry Pi
 
+description: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API, and plays audio responses.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory project for developers interested in integrating a Chatbot (namely ChatGPT) into Raspberry Pi projects.
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Run Llama 3 on a Raspberry Pi 5 using ExecuTorch
 
+description: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for anyone interested in running the Llama 3 model on a Raspberry Pi 5, and learning about techniques for running large language models (LLMs) in an embedded environment.
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build embedded Linux applications on an Arm server
 
+description: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring it to a Raspberry Pi.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for software developers who want to reduce compile time for embedded Linux software projects.
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Get started with the Raspberry Pi 4
 
+description: Learn how to build and run multiple software examples on the Raspberry Pi 4, including TensorFlow and Docker applications, and compare its performance to Arm cloud servers.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for software developers interested in the Raspberry Pi 4.
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/1_overview.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/1_overview.md
@@ -7,7 +7,7 @@ layout: learningpathall
 ---
 
 ## Overview
-Performance tuning is just as important for kernel modules as it is for user-space applications. Arm Streamline is a powerful profiling tool that helps you find performance bottlenecks, hotspots, and memory issues - even inside the Linux kernel. In this Learning Path, you'll learn how to use Arm Streamline to profile a simple kernel module on Arm-based systems. You'll see how profiling can reveal optimization opportunities and help you improve your module's efficiency.
+Performance tuning is as important for kernel modules as it is for user-space applications. Arm Streamline is a powerful profiling tool that helps you find performance bottlenecks, hotspots, and memory issues - even inside the Linux kernel. In this Learning Path, you'll learn how to use Arm Streamline to profile a simple kernel module on Arm-based systems. You'll see how profiling can reveal optimization opportunities and help you improve your module's efficiency.
 ## Benefits of profiling Linux kernel modules with Arm Streamline
 
 Kernel modules often operate in performance-critical paths, such as device drivers or networking subsystems. Even a small inefficiency in a module can affect the overall system performance.
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/4_sl_profile_oot.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/4_sl_profile_oot.md
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/6_sl_profile_intree.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/6_sl_profile_intree.md
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
