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 language | English |
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Pages (from-to) | 222-225 |
Number of pages | 4 |
Journal | IEEE Microwave and Wireless Components Letters |
Volume | 32 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Mar 2022 |
Externally published | Yes |
Keywords
- Doppler effect
- Doppler radar
- Gesture recognition
- Hardware
- Indexes
- Neurons
- Radar
- Radar gesture recognition
- Spiking networks