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Leverage the Weight Compression of the Arm Ethos-U NPU Pruning & clustering improves performance on memory-bound models
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Classroom Contents
Creating End-to-End TinyML Applications for Ethos-U NPU in the Cloud
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- 1 Intro
- 2 Creating TinyML applications is difficult
- 3 Main software stack to run ML on Cortex-M today Cortex-Mis robust and flexible, Ethos-U is dedicated ML accelerator
- 4 Key steps to run an inference on Cortex-M Pre-processing and post-processing is specific to a model
- 5 Hardware supported vs non-supported operator in the NN Example of the benefit of using hardware supported operators on Ethos-U
- 6 Leverage the Weight Compression of the Arm Ethos-U NPU Pruning & clustering improves performance on memory-bound models
- 7 We provide a number of example applications!
