On the Use of Spiking Neural Networks for Ultralow-Power Radar Gesture Recognition

Ali Safa*, Andre Bourdoux, Ilja Ocket, Francky Catthoor, Georges G.E. Gielen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultralow-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 an evaluation code to help future research.

Original languageEnglish
Pages (from-to)222-225
Number of pages4
JournalIEEE Microwave and Wireless Components Letters
Volume32
Issue number3
DOIs
Publication statusPublished - 1 Mar 2022
Externally publishedYes

Keywords

  • Doppler effect
  • Doppler radar
  • Gesture recognition
  • Hardware
  • Indexes
  • Neurons
  • Radar
  • Radar gesture recognition
  • Spiking networks

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