Introduction to TinyML on Arm using PyTorch and ExecuTorch
Introduction
Overview
Install ExecuTorch
Set up the Corstone-320 FVP
Build a simple PyTorch model
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  40 min
Last updated:  27 Feb 2025
Skill level:
  Introductory
Reading time:
  40 min
Last updated:
  27 Feb 2025
Author:Dominica Abena O. Amanfo
Arm IP:Cortex-A Cortex-M
Tags: ML Linux Arm Virtual Hardware Fixed Virtual Platform Python PyTorch ExecuTorch Arm Compute Library GCC
Author:
  • Dominica Abena O. Amanfo
Arm IP:
Cortex-A Cortex-M
Tags:
ML Linux Arm Virtual Hardware Fixed Virtual Platform Python PyTorch ExecuTorch Arm Compute Library GCC

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.

What will you learn?

Upon completion of this learning path, you will be able to:

  • 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

Before starting, you will need the following:

  • Basic knowledge of Machine Learning concepts
  • A Linux computer
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