Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks

YuFei Guo
* ORCID logo ,
Yuanpei Chen
,
Liwen Zhang
,
YingLei Wang
,
Xiaode Liu
,
Xinyi Tong
,
Yuanyuan Ou
,
Xuhui Huang
,
Zhe Ma
Version 1 : Received: 17 December 2023 / Approved: 18 December 2023 / Online: 18 December 2023 (10:01:47 CET)

How to cite: Guo, Y.; Chen, Y.; Zhang, L.; Wang, Y.; Liu, X.; Tong, X.; Ou, Y.; Huang, X.; Ma, Z. InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks. Preprints 2023, 2023121318. https://doi.org/10.20944/preprints202312.1318.v1 Guo, Y.; Chen, Y.; Zhang, L.; Wang, Y.; Liu, X.; Tong, X.; Ou, Y.; Huang, X.; Ma, Z. InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks. Preprints 2023, 2023121318. https://doi.org/10.20944/preprints202312.1318.v1

Abstract

The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its ``Hard Reset" mechanism for the firing activity would ignore the difference among membrane potentials when the membrane potential is above the firing threshold, causing information loss. Meanwhile, quantifying the membrane potential to 0/1 spikes at the firing instants will inevitably introduce the quantization error thus bringing about information loss too. To address these problems, we propose to use the ``Soft Reset" mechanism for the supervised training-based SNNs, which will drive the membrane potential to a dynamic reset potential according to its magnitude, and Membrane Potential Rectifier (MPR) to reduce the quantization error via redistributing the membrane potential to a range close to the spikes. Results show that the SNNs with the ``Soft Reset" mechanism and MPR outperform their vanilla counterparts on both static and dynamic datasets.

Keywords

Spiking Neural Network; Information Loss; Soft Reset; Quantization Error; Membrane Potential rectificater

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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