research-article

Q-SNNs: Quantized Spiking Neural Networks

Authors:

Ammar Belatreche,

Yang YangAuthors Info & Claims

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

Pages 8441 - 8450

https://doi.org/10.1145/3664647.3681186

Published: 28 October 2024 Publication History

Abstract

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence. However, the current focus within the SNN community prioritizes accuracy optimization through the development of large-scale models, limiting their viability in resource-constrained and low-power edge devices. To address this challenge, we introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials. By significantly compressing these two key elements, the proposed Q-SNNs substantially reduce both memory usage and computational complexity. Moreover, to prevent the performance degradation caused by this compression, we present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory. Experimental evaluations on various datasets, including static and neuromorphic, demonstrate that our Q-SNNs outperform existing methods in terms of both model size and accuracy. These state-of-the-art results in efficiency and efficacy suggest that the proposed method can significantly improve edge intelligent computing.

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Cited By

Sartori Locatelli PAit Fares SMartini Colombo DEl-Sankary KFreund M(2025)Multi-Task Spiking Neural Network for Simultaneous Vapor Recognition and Concentration EstimationIEEE Access10.1109/ACCESS.2025.354140813(29847-29863)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541408
Wang SZhang DBelatreche AXiao YQing HWei WZhang MYang Y(2025)Ternary spike-based neuromorphic signal processing systemNeural Networks10.1016/j.neunet.2025.107333(107333)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2025.107333
Zhang XXie CChen ZLiu D(2024)Lightweight Remote Sensing Object Detection Model Based on Spiking Neural Networks2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)10.1109/PRAI62207.2024.10827660(327-334)Online publication date: 15-Aug-2024
https://doi.org/10.1109/PRAI62207.2024.10827660
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Index Terms

Q-SNNs: Quantized Spiking Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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cover image ACM Conferences

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

October 2024

11719 pages

ISBN:9798400706868

DOI:10.1145/3664647

General Chairs:
Jianfei Cai
Monash University, Australia
,
Mohan Kankanhalli
NUS, Singapore
,
Balakrishnan Prabhakaran
UT Dallas, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Program Chairs:
Ramanathan Subramanian
University of Canberra & IIT Ropar, Australia
,
Liang Zheng
Australian National University, Australia
,
Vivek K. Singh
Rutgers University, USA
,
Pablo Cesar
Centrum Wiskunde & Informatica, Netherlands
,
Lexing Xie
Australian National University, Australia
,
Dong Xu
University of Hong Kong, Hong Kong

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Published: 28 October 2024

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MM '24: The 32nd ACM International Conference on Multimedia

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Cited By

Sartori Locatelli PAit Fares SMartini Colombo DEl-Sankary KFreund M(2025)Multi-Task Spiking Neural Network for Simultaneous Vapor Recognition and Concentration EstimationIEEE Access10.1109/ACCESS.2025.354140813(29847-29863)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541408
Wang SZhang DBelatreche AXiao YQing HWei WZhang MYang Y(2025)Ternary spike-based neuromorphic signal processing systemNeural Networks10.1016/j.neunet.2025.107333(107333)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2025.107333
Zhang XXie CChen ZLiu D(2024)Lightweight Remote Sensing Object Detection Model Based on Spiking Neural Networks2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)10.1109/PRAI62207.2024.10827660(327-334)Online publication date: 15-Aug-2024
https://doi.org/10.1109/PRAI62207.2024.10827660
Qiutian L(2024)Lightweight Computational Model for Keyword Spotting2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)10.1109/ICCWAMTIP64812.2024.10873662(01-05)Online publication date: 14-Dec-2024
https://doi.org/10.1109/ICCWAMTIP64812.2024.10873662

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