Neuromorphic Edge AI for Minimalistic Sensing of Touch Gestures

Demonstrator VCD1.3 of the EdgeAI project integrates a minimalistic sensing approach with a neuromorphic processor for real-time gesture recognition at the edge. Developed by ams OSRAM and SynSense, the system processes optical interferometric signals to classify three gestures—swipe left, swipe right, and tap—using Spiking Neural Networks (SNNs). It achieves 40 fps with ultra-low power consumption (50 µW) and a compact memory footprint (~100 KB). The sensor relies on self-mixing interferometry, where a near-infrared VCSEL (Vertical-Cavity Surface-Emitting Laser) reflects off a target (the touch interface) and re-enters the laser cavity, producing measurable interferometric disturbances. The hardware, established during the first two years of the project, is undergoing extensive validation in year three. We conduct cross-user generalization tests, noise robustness evaluations, and comparisons with conventional CNN models. Next steps include finalizing verification activities—such as model stability analysis and strategy development—and expanding CNN benchmarking through failure case analysis and performance metrics. These efforts aim to enhance system reliability and broaden applicability in real-world human–machine interfaces.