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Efficient deployment of ML models on low-power embedded systems has been a significant challenge for a number of years. At the same time, these embedded SoCs are all around us—from everyday appliances to the latest smart glasses.
ExecuTorch is a PyTorch-native framework for deploying neural networks on resource-constrained systems. In this session, we show how to build an end-to-end speech recognition application using PyTorch and ExecuTorch—from training a Transformer-based neural network in PyTorch, through quantization, all the way to deployment on a low-power embedded device.
We will introduce the key ExecuTorch APIs for quantization and explain how models are transformed and lowered into a form that can run efficiently on a device. The application is running on the Alif Ensemble E8 SoC, the first implementation of the leading Arm® Ethos-U85 NPU which brings native support for Transformer models to the ultra-low power domain.
Join the experts from Arm and Alif Semiconductor to see how we are bridging the gap between PyTorch and embedded deployment—and how you can bring PyTorch models to silicon-accelerated, ultra-low-power systems.
George Gekov is a Staff Software Engineer in Arm’s Machine Learning team, where he focuses on machine learning inference on embedded systems. He has extensive experience deploying neural networks on resource-constrained devices with Neural Processing Units (NPUs) to enable hardware-accelerated... Read More →
