# Visualize Ethos-U NPU performance with ExecuTorch on Arm FVPs

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/)
- [Overview](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/2-overview/)
- [Understand the ExecuTorch workflow](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow/)
- [Set up your ExecuTorch environment](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/4-env-setup-execut/)
- [Set up the Corstone-320 Fixed Virtual Platform](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/5-env-setup-fvp/)
- [Deploy and run Mobilenet V2 on the Corstone-320 FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/6-run-model/)
- [Enable GUI and deploy a model on Corstone-320 FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/7-configure-fvp-gui/)
- [Evaluate Ethos-U Performance](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/8-evaluate-output/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_next-steps/)

## About this Learning Path

| Skill level:     | Introductory         |
|------------------|---------------------|
| Reading time:    | 2 hrs               |
| Last updated:    | 13 Aug 2026         |

| Author:          | Waheed Brown, Arm [GitHub](https://github.com/https://github.com/armwaheed) [LinkedIn](https://linkedin.com/in/https://www.linkedin.com/in/waheedbrown/) |
|------------------|---------------------|
| Arm IP:          | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) [Cortex-M](https://support.arm.com/?tab=compute-ip&Product%20Type=Microcontrollers) [Ethos-U](https://support.arm.com/?tab=compute-ip&Product%20Type=Neural%20Processing%20Units) |

### Who is this for?
This is an introductory topic for developers and data scientists who are new to TinyML and want to visualize ExecuTorch model performance on virtual Arm hardware.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Identify Arm-based targets suitable for TinyML workloads
- Install and configure Fixed Virtual Platforms (FVPs)
- Deploy a TinyML model using ExecuTorch on a Corstone-320 FVP
- Visualize model execution using the FVP graphical interface

### Prerequisites
Before starting, you will need the following:
- Familiarity with basic machine learning concepts
- A Linux or macOS computer with Python 3 installed

### Summary
You’ll use ExecuTorch and the Corstone-320 FVP to explore TinyML execution with Ethos-U. First, you’ll set up ExecuTorch, install the FVP, and export a model with the ahead-of-time workflow. Then, you’ll run MobileNet V2 and use the FVP graphical interface to visualize CPU and NPU activity without physical hardware.

### Frequently asked questions
#### What additional setup do I need before running the FV on macOS?
Follow the guidance in the [FVPs-on-Mac GitHub repository](https://github.com/Arm-Examples/FVPs-on-Mac/) before launching the Corstone-320 FVP.

#### Where is the MobileNet V2 example located in the ExecuTorch repository?
The Python code for MobileNet V2 is in `executorch/examples/models/mobilenet_v2/model.py`. Use this example when deploying the model to the Corstone-320 FVP.

#### How do I run the MobileNet V2 example on the Corstone-320 FVP?
Use the provided `run.sh` script with the additional parameters shown in the steps after completing the environment and FVP setup. Run the script from the `executorch` repository.

#### How do I know the Corstone-320 FVP installed and started correctly?
The FVP should launch without errors and present its graphical interface.

#### How do I verify that execution uses the simulated Ethos-U NPU?
Use the FVP graphical interface to visualize model execution and look for activity corresponding to CPU and NPU components.