Papers › A 2-$μ$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
A 2-$μ$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
Ali Safa, André Bourdoux, Ilja Ocket, Francky Catthoor, Georges G. E. Gielen
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional energy- and area-hungry deep learning methods, SNNs are significantly more energy efficient and can be deployed in the growing number of compact SNN accelerator chips, making them a better solution for ubiquitous IoT applications. We propose a novel SNN strategy for radar gesture recognition, achieving more than 91% of accuracy on two different radar datasets. Our work significantly differs from previous approaches as 1) we use a novel radar-SNN training strategy, 2) we use quantized weights, enabling power-efficient implementation in real-world SNN hardware, and 3) we report the SNN energy consumption per classification, clearly demonstrating the real-world feasibility and power savings induced by SNN-based radar processing. We release evaluation code to help future research.
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