Deploy ExecuTorch firmware on NXP FRDM i.MX 93 for Ethos-U65 acceleration
Introduction
Understand ExecuTorch deployment on NXP with Ethos-U
Boot the NXP FRDM i.MX 93 board
Set up the ExecuTorch build environment
Build and install ExecuTorch
Build ExecuTorch models for Ethos-U65
Build Cortex-M33 firmware for ExecuTorch
Deploy and test on FRDM-IMX93
Next Steps

  Log an issue

  Fork and edit

  Discuss on Discord

About this Learning Path

Skill level:  Introductory
Reading time:  2 hrs
Last updated:  22 Jun 2026
Skill level:
  Introductory
Reading time:
  2 hrs
Last updated:
  22 Jun 2026
Authors:Waheed Brown, Arm
Fidel Makatia Omusilibwa
Arm IP:Cortex-A Cortex-M Ethos-U
Tags: ML Linux macOS Baremetal Python PyTorch ExecuTorch Arm Compute Library GCC
Authors:
  • Waheed Brown, Arm
  • Fidel Makatia Omusilibwa
Arm IP:
Cortex-A Cortex-M Ethos-U
Tags:
ML Linux macOS Baremetal Python PyTorch ExecuTorch Arm Compute Library GCC

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.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Bring up a custom ExecuTorch `executor_runner` firmware on the FRDM i.MX 93 Cortex-M33 using Linux RemoteProc
  • Compile an ExecuTorch `.pte` model for Ethos-U65 and run inference with NPU acceleration
  • Understand how heterogeneous Arm systems split responsibilities across application cores, microcontrollers, and NPUs

Prerequisites

Before starting, you will need the following:

  • An NXP FRDM i.MX 93 development board
  • A USB Mini-B to USB Type-A cable, or a USB Mini-B to USB Type-C cable
  • Completion of Use Linux on an NXP FRDM i.MX 93 board (Linux setup, login access, and file transfer)
  • Basic knowledge of Machine Learning concepts
  • A host computer to compile ExecuTorch libraries
Next