Who is this for?
This is an advanced topic for embedded developers who want to deploy a neural network model to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time (AOT) compiler on an Arm-based cloud instance
- Build ExecuTorch static libraries for bare-metal Cortex-M55 targets
- Configure CMSIS project files, memory layout, and linker scripts for an ML workload on the Alif Ensemble E8
- Run real-time image classification inference on the Ethos-U85 NPU and verify results using SEGGER Real-Time Transfer (RTT)
Prerequisites
Before starting, you will need the following:
- Experience with C/C++ and embedded development concepts
- An
Alif Ensemble E8 DevKit
with a USB-C cable
- A SEGGER J-Link debug probe (included in the DevKit)
- A development machine running macOS on Apple Silicon with Visual Studio Code installed
- An AWS account or access to an Arm-based cloud instance for native Arm compilation