Visualize Ethos-U NPU performance with ExecuTorch on Arm FVPs
Introduction
Overview
Understand the ExecuTorch workflow
Set up your ExecuTorch environment
Set up the Corstone-320 Fixed Virtual Platform
Deploy and run Mobilenet V2 on the Corstone-320 FVP
Enable GUI and deploy a model on Corstone-320 FVP
Evaluate Ethos-U Performance
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  2 hrs
Last updated:  05 Sep 2025
Skill level:
  Introductory
Reading time:
  2 hrs
Last updated:
  05 Sep 2025
Author:Waheed Brown, Arm
Arm IP:Cortex-A Cortex-M Ethos-U
Tags: ML Linux macOS Arm Virtual Hardware FVP Python PyTorch ExecuTorch Arm Compute Library GCC Docker
Author:
  • Waheed Brown, Arm
Arm IP:
Cortex-A Cortex-M Ethos-U
Tags:
ML Linux macOS Arm Virtual Hardware FVP Python PyTorch ExecuTorch Arm Compute Library GCC Docker

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
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