Updates

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/2-env-setup.md

@@ -8,7 +8,7 @@ weight: 3
 layout: "learningpathall"
 ---
 
-In this section, you will prepare a development environment to compile a machine learning model.
+In this section, you prepare a development environment to compile and run a machine learning model with ExecuTorch.
 
 ## Introduction to ExecuTorch
 
@@ -18,7 +18,7 @@ ExecuTorch is a lightweight runtime designed for efficient execution of PyTorch
 
 These instructions have been tested on Ubuntu 22.04, 24.04, and on Windows Subsystem for Linux (WSL).
 
-Python3 is required and comes installed with Ubuntu, but some additional packages are needed:
+Python 3 is required and comes installed with Ubuntu, but some additional packages are needed:
 
 ```bash
 sudo apt update
@@ -36,7 +36,7 @@ source $HOME/executorch-venv/bin/activate
 The prompt of your terminal now has `(executorch)` as a prefix to indicate the virtual environment is active.
 
 
-## Install Executorch
+## Install ExecuTorch
 
 From within the Python virtual environment, run the commands below to download the ExecuTorch repository and install the required packages:
 
@@ -74,6 +74,6 @@ pip list | grep executorch
 executorch             1.1.0a0+1883128
 ```
 
-## Next Steps
+## Next steps
 
-Proceed to the next section to learn about and set up the virtualized hardware.
+Proceed to the next section to set up the virtualized hardware.

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/3-env-setup-fvp.md

@@ -8,22 +8,22 @@ weight: 5 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 
-In this section, you will run scripts to set up the Corstone-320 reference package.
+In this section, you run scripts to set up the Corstone-320 reference package.
 
-The Corstone-320 Fixed Virtual Platform (FVP) is a pre-silicon software development environment for Arm-based microcontrollers. It provides a virtual representation of hardware, allowing developers to test and optimize software before actual hardware is available. Designed for AI and machine learning workloads, it includes support for Arm's Ethos-U NPU and Cortex-M processors, making it ideal for embedded AI applications. The FVP accelerates development by enabling early software validation and performance tuning in a flexible, simulation-based environment.
+The Corstone-320 Fixed Virtual Platform (FVP) is a pre-silicon software development environment for Arm-based microcontrollers. It provides a virtual representation of hardware so you can test and optimize software before boards are available. Designed for AI and machine learning workloads, it includes support for Arm Ethos-U NPUs and Cortex-M processors, which makes it well-suited to embedded AI applications. The FVP accelerates development by enabling early software validation and performance tuning in a flexible, simulation-based environment.
 
 The Corstone reference system is provided free of charge, although you will have to accept the license in the next step. For more information on Corstone-320, check out the [official documentation](https://developer.arm.com/documentation/109761/0000?lang=en).
 
-## Corstone-320 FVP Setup for ExecuTorch
+## Set up Corstone-320 FVP for ExecuTorch
 
-Run the FVP setup script in the ExecuTorch repository.
+Run the FVP setup script in the ExecuTorch repository:
 
 ```bash
 cd $HOME/executorch
 ./examples/arm/setup.sh --i-agree-to-the-contained-eula
 ```
 
-After the script has finished running, it prints a command to run to finalize the installation. This step adds the FVP executables to your system path.
+When the script completes, it prints a command to finalize the installation by adding the FVP executables to your `PATH`:
 
 ```bash
 source $HOME/executorch/examples/arm/ethos-u-scratch/setup_path.sh

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model.md

@@ -36,7 +36,7 @@ class SimpleNN(torch.nn.Module):
         return out
 
 # Create the model instance
-input_size = 10   # example input features size
+input_size = 10   # example input feature size
 hidden_size = 5   # hidden layer size
 output_size = 2   # number of output classes
 
@@ -52,7 +52,7 @@ ModelInputs = x
 print("Model successfully exported to simple_nn.pte")
 ```
 
-## Running the model on the Corstone-320 FVP
+## Run the model on the Corstone-320 FVP
 
 The final step is to take the Python-defined model and run it on the Corstone-320 FVP. This was done upon running the `run.sh` script in a previous section. To wrap up the Learning Path, you will perform these steps separately to better understand what happened under the hood. Start by setting some environment variables that are used by ExecuTorch.
 
@@ -61,7 +61,7 @@ export ET_HOME=$HOME/executorch
 export executorch_DIR=$ET_HOME/build
 ```
 
-Then, generate a model file on the `.pte` format using the Arm examples. The Ahead-of-Time (AoT) Arm compiler will enable optimizations for devices like the Grove Vision AI Module V2 and the Corstone-320 FVP. Run it from the ExecuTorch root directory.
+Generate a model in ExecuTorch `.pte` format using the Arm examples. The AoT Arm compiler enables optimizations for devices such as the Grove Vision AI Module V2 and the Corstone-320 FVP. Run the compiler from the ExecuTorch root directory:
 
 ```bash
 cd $ET_HOME
@@ -90,7 +90,7 @@ cmake --build $ET_HOME/examples/arm/executor_runner/cmake-out --parallel -- arm_
 
 ```
 
-Now run the model on the Corstone-320 with the following command:
+Run the model on Corstone-320:
 
 ```bash
 FVP_Corstone_SSE-320 \
@@ -104,9 +104,7 @@ FVP_Corstone_SSE-320 \
 ```
 
 {{% notice Note %}}
-
-The argument `mps4_board.visualisation.disable-visualisation=1` disables the FVP GUI. This can speed up launch time for the FVP.
-
+The argument `mps4_board.visualisation.disable-visualisation=1` disables the FVP GUI and can speed up launch time
 {{% /notice %}}
 
 Observe that the FVP loads the model file.
@@ -119,4 +117,4 @@ I [executorch:arm_executor_runner.cpp:412] Model in 0x70000000 $
 I [executorch:arm_executor_runner.cpp:414] Model PTE file loaded. Size: 3360 bytes.
 ```
 
-You have now set up your environment for TinyML development on Arm, and tested a small PyTorch and ExecuTorch Neural Network. In the next Learning Path of this series, you will learn about optimizing neural networks to run on Arm.
+You have now set up your environment for TinyML development on Arm and tested a small PyTorch model with ExecuTorch on the Corstone-320 FVP. In the next Learning Path, you learn how to optimize neural networks to run efficiently on Arm.

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

@@ -6,10 +6,10 @@ 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.
 
 learning_objectives:
-    - Describe what differentiates TinyML from other AI domains.
-    - Describe the benefits of deploying AI models on Arm-based edge devices.
-    - Identify suitable Arm-based devices for TinyML applications.
-    - Set up and configure a TinyML development environment using ExecuTorch and Corstone-320 Fixed Virtual Platform (FVP).
+    - Describe what differentiates TinyML from other AI domains
+    - Describe the benefits of deploying AI models on Arm-based edge devices
+    - Identify suitable Arm-based devices for TinyML applications
+    - Set up and configure a TinyML development environment using ExecuTorch and Corstone-320 Fixed Virtual Platform (FVP)
 
 prerequisites:
     - Basic knowledge of Machine Learning concepts