Merge pull request #3165 from madeline-underwood/metaserver3

pareenaverma · web-flow · commit bb71a7e3a8c7 · 2026-04-15T13:16:07.000-04:00
Enhance documentation across multiple learning paths by adding descri…
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
@@ -23,7 +23,7 @@ For step-by-step guidance on setting up Streamline on your host machine, follow
 
 ## Push Gator to the target and make a capture
 
-Once Streamline is installed on the host machine, you can capture trace data of our Linux kernel module. On Linux, the binaries will be installed where you extracted the package.
+Once Streamline is installed on the host machine, you can capture trace data of the Linux kernel module. On Linux, the binaries will be installed where you extracted the package.
 
 To communicate with the target device, Streamline uses a background service called gatord. This daemon must be running on the target before you can capture trace data. Streamline provides two pre-built gatord binaries in the installation directory: one for Armv7 (AArch32) and one for Armv8 or later (AArch64) systems.
 
@@ -77,13 +77,11 @@ Start the capture and enter a name and location for the capture file. Streamline
 
 ## Analyze the capture and inspect the code
 
-Once the capture is stopped, Streamline automatically analyzes the collected data and provides insights to help you identify performance issues and bottlenecks. This section describes how to view these insights, starting with locating the functions related to our kernel module and narrowing down to the exact lines of code that may be responsible for the performance problems.
+Once the capture is stopped, Streamline automatically analyzes the collected data and provides insights to help you identify performance issues and bottlenecks. This section describes how to view these insights, starting with locating the functions related to the kernel module and narrowing down to the exact lines of code that may be responsible for the performance problems.
 
 Open the **Functions** tab. In the counters list, select one of the counters you enabled earlier in the **counter configuration** dialog, as shown:
-<!--
-Alt text: Screenshot of the Streamline profiling tool interface showing the process of selecting a data source for counters. The main panel displays a list of available data sources with one highlighted, and a sidebar provides navigation options. The environment is a typical software profiling application window with a neutral, professional tone. Visible text includes Counter selection and labels for different data sources.
--->
-![Counter selection alt-text#center](./images/img07_select_datasource.png "Select data source for counters")
+
+![Streamline counter selection interface showing a dropdown list of available data sources for performance counters. The main panel displays counter options with one highlighted for selection. The interface uses neutral technical design with labeled sections for Counter selection and data source names.#center](./images/img07_select_datasource.png "Select data source for counters")
 
 In the **Functions** tab, look for the function `char_dev_cache_traverse()`. You'll see that it has the highest L1 Cache refill rate, which is expected for this example. Check the **Image** column on the right. This should show your module file name, `mychardrv.ko`. This confirms that Streamline is capturing performance data for your kernel module.
 
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
@@ -25,7 +25,7 @@ After you select **vmlinux** as the capture image in the **Capture settings** di
 Now, start the capture session. Streamline collects profiling data from the in-tree kernel driver and the rest of the kernel. When the capture completes, analyze the results using the same workflow as before. 
 
 You can review function-level metrics, explore call paths, and identify performance hotspots within both your driver and the kernel.
-![Streamline function tab displaying profiling data for vmlinux. The main area shows a list of kernel functions with columns for metrics such as sample count and percentage of total samples. The function names are clearly listed, and the selected function is highlighted. The interface uses a neutral, technical design typical of performance profiling tools. Visible text includes Function, Sample Count, and Percentage. The wider environment includes other Streamline tabs and configuration options, reinforcing a focused, analytical workflow. alt-text #center](./images/img12_vmlinux_function_tab.png "Streamline function tab displaying profiling data for vmlinux")
+![Streamline function tab displaying a list of kernel functions with sample count and percentage columns. The selected function is highlighted in the list. The interface uses a neutral technical design with clear column headers showing Function, Sample Count, and Percentage labels.#center](./images/img12_vmlinux_function_tab.png "Streamline function tab displaying profiling data for vmlinux")
 
 Because you selected the `vmlinux` image, Streamline displays both your driver functions and all other kernel functions sampled during profiling. This lets you see how your driver interacts with the rest of the kernel.
 
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
@@ -1,6 +1,8 @@
 ---
 title: Profile the Linux kernel with Arm Streamline
 
+description: Learn how to profile Linux kernel modules using Arm Streamline to identify performance bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension (SPE) for deeper insights.
+
 
 minutes_to_complete: 60
 
