Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU acceleration.
Learn how to train a CNN image classification model using PyTorch, convert it to ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices.
Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms, deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical interface.
Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and run inference on the Ethos-U85 NPU.
Evaluation Conditions
Image Classification · Alif DK-E8 · INT8 · 224×224 · ImageNet-1k (val)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 35.17ms-ms | ||
| End to end latency p90 | 35.17ms- |
| Peak memory | 2.39MB-MB |
| Frames per second | 28.43fps-fps |
| Accuracy - Top 1 | 72.93%75.26% | −2.33% |
| Accuracy - Top 5 | 91.23%92.57% | −1.34% |
| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | ImageNet-1k (val) | ImageNet-1k (val) |
| Sample count | 50,000 | 50,000 |
| Batch size | 1 | 1 |
| Input resolution |
The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.
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| 224x224 |
224x224 |
| Runs | 1 | 100 |
| Warmup runs | N/A | 10 |