{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-2-m-j-12-class-91-accuracy-spiking-neural","title":"A 2-$μ$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition","arxiv_id":"2108.02669","date":"2021-08-05","proceeding":null,"authors":["Ali Safa","André Bourdoux","Ilja Ocket","Francky Catthoor","Georges G. E. Gielen"],"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.","url_abs":"https://arxiv.org/abs/2108.02669v2","url_pdf":"https://arxiv.org/pdf/2108.02669v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-2-m-j-12-class-91-accuracy-spiking-neural","repo_url":"https://github.com/ali20480/Convolutional_SNN_RadarGesture8GHz","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}