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Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing

Junxiu Liu, Yanhu Wang, Yuling Luo Orcid Logo, Shunsheng Zhang, Dong Jiang, Yifan Hua, Sheng Qin, Scott Yang Orcid Logo

Neural Processing Letters, Volume: 55, Issue: 6, Pages: 7155 - 7173

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1007/s11063-023-11255-8

Abstract

Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from la...

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Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63218
first_indexed 2023-04-20T08:48:39Z
last_indexed 2024-11-15T18:01:10Z
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spelling 2024-07-29T14:06:12.4714683 v2 63218 2023-04-20 Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2023-04-20 MACS Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from large hardware resource consumption due to the high complexity of computational units. In this paper, a novel hardware SNN system based on stochastic computing is proposed to address this problem. Pair-based spiking-timing-dependent plasticity, coupled with integrate-and-fire neurons are employed to design the SNN. Stochastic computing can simplify the computational components of multipliers, adders, and subtractors in conventional hardware SNNs, hence reduce the hardware resource cost. Experimental results show that compared with the state-of-the-art approaches the proposed SNN system reduces the resource consumption by 58.0% (especially registers by ≥ 65.6%). In the meantime, the maximum normalized root mean square error between the proposed hardware and others is only 0.0097, which can maintain the behaviours of SNN. This work provides a beneficial alternative to the large-scale hardware SNN implementations. Journal Article Neural Processing Letters 55 6 7155 7173 Springer Science and Business Media LLC 1370-4621 1573-773X Spiking neural networks, Pair-based spiking-timing-dependent plasticity, integrate-and-fire neurons, stochastic computing 1 12 2023 2023-12-01 10.1007/s11063-023-11255-8 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This research is supported by the National Natural Science Foundation of China under Grant 61976063, the Guangxi Natural Science Foundation under Grant 2022GXNSFFA035028, research fund of Guangxi Normal University under Grant 2021JC006, the AI+Education research project of Guangxi Humanities Society Science Development Research Center under Grant ZXZJ202205. 2024-07-29T14:06:12.4714683 2023-04-20T09:46:06.3018350 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Junxiu Liu 1 Yanhu Wang 2 Yuling Luo 0000-0002-0117-4614 3 Shunsheng Zhang 4 Dong Jiang 5 Yifan Hua 6 Sheng Qin 7 Scott Yang 0000-0002-6618-7483 8 63218__27308__570045e260f04b418a8e7d2334703224.pdf 63218.pdf 2023-05-03T09:27:16.1237172 Output 813938 application/pdf Accepted Manuscript true 2024-04-06T00:00:00.0000000 true eng
title Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
spellingShingle Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
Scott Yang
title_short Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
title_full Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
title_fullStr Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
title_full_unstemmed Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
title_sort Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Junxiu Liu
Yanhu Wang
Yuling Luo
Shunsheng Zhang
Dong Jiang
Yifan Hua
Sheng Qin
Scott Yang
format Journal article
container_title Neural Processing Letters
container_volume 55
container_issue 6
container_start_page 7155
publishDate 2023
institution Swansea University
issn 1370-4621
1573-773X
doi_str_mv 10.1007/s11063-023-11255-8
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from large hardware resource consumption due to the high complexity of computational units. In this paper, a novel hardware SNN system based on stochastic computing is proposed to address this problem. Pair-based spiking-timing-dependent plasticity, coupled with integrate-and-fire neurons are employed to design the SNN. Stochastic computing can simplify the computational components of multipliers, adders, and subtractors in conventional hardware SNNs, hence reduce the hardware resource cost. Experimental results show that compared with the state-of-the-art approaches the proposed SNN system reduces the resource consumption by 58.0% (especially registers by ≥ 65.6%). In the meantime, the maximum normalized root mean square error between the proposed hardware and others is only 0.0097, which can maintain the behaviours of SNN. This work provides a beneficial alternative to the large-scale hardware SNN implementations.
published_date 2023-12-01T20:21:34Z
_version_ 1821347670623518720
score 11.04748

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