EDUCATION & TRAINING
Quantising event-camera networks to run under 1MB on a Cortex-M7
Dev.to Machine Learning
About This Tutorial
TL;DR: I shrunk a gesture-recognition model for a Prophesee EVK4 event camera from 4.2MB down to 780KB so it could run on an STM32H7 at 15ms per inference. The trick was not the quantisation itself, it was rethinking what an "image" even means when your sensor produces events instead of frames. So, the thing is, most computer vision tutorials assume you start with a tensor of shape [B, 3, H, W] and end with a classification head. Event cameras break that assumption on day one. A Prophesee sensor doesn't give you frames at 30fps.