ConvSNN: A surrogate gradient spiking neural framework for radar gesture recognition

Abstract

Spiking neural networks (SNNs) have recently gained large interest for edge-AI applications due to their low latency and ultra-low energy consumption. Unlike DNNs, SNNs communicate information using spike trains. As the derivative of spike trains are highly ill-defined, the use of surrogate gradients has been proposed as an efficient method for training SNNs. Still, the lack of open-source SNN softwares and the limited range of demonstrated SNN applications slows down a wider SNN adoption. We release our ConvSNN framework, demonstrating the novel applicability of quantized-weight SNNs for radar gesture recognition. Our framework will facilitate future research in the SNN area.