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arXiv · 2108.02669

A 2-$\mu$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
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Abstract

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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Ali Safa, André Bourdoux, Ilja Ocket, Francky Catthoor, Georges G. E. Gielen. 2021-08-05. A 2-$\mu$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition. https://arxiv.org/abs/2108.02669

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