Arm backend: Minimal example of pruning #15851
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Pull Request Overview
This PR introduces a comprehensive tutorial on neural network pruning for the Arm Ethos-U NPU, demonstrating how to prune a model and measure the resulting performance improvements. The example uses a simple MNIST classifier and shows the complete workflow from training to deployment.
Key changes:
- Adds a Jupyter notebook tutorial demonstrating pruning workflow with PyTorch and ExecuTorch
- Updates performance monitoring to track MAC and Weight Decoder activity for Ethos-U85
- Shows 3x+ inference speedup and significant memory reduction through pruning
Reviewed Changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 7 comments.
| File | Description |
|---|---|
| examples/arm/pruning_minimal_example.ipynb | Complete tutorial notebook showing how to prune a neural network, quantize it, and deploy to Ethos-U NPU with performance analysis |
| examples/arm/executor_runner/arm_perf_monitor.cpp | Adds MAC_ACTIVE and WD_ACTIVE PMU counter tracking for Ethos-U85 to support detailed performance analysis |
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examples/arm/pruning_minimal_example.ipynb:1
- The flag '--debug-force-regor' appears to contain a typo. Verify if this should be '--debug-force-regor' or if it's a misspelling of a valid flag like '--debug-force-regen' or '--debug-force-reorder'.
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@gggekov the label "topic: not user facing" is not used anymore so no need to set it it was replaced with "release notes: arm" or "release notes: none" |
Explain how to prune a NN and the associated uplift in performance when running on the Ethos-U NPU. Change-Id: Ib68513e5b4cb7ceef280b6fe089985e9948a8140
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Explain how to prune a NN and the associated uplift in performance when running on the Ethos-U NPU.
cc @freddan80 @per @zingo @oscarandersson8218 @digantdesai