No Cover Image

Journal article 392 views

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

Swansea University Author: Scott Yang Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

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...

Full description

Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63218
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2023-04-20T08:48:39Z
last_indexed 2023-04-20T08:48:39Z
id cronfa63218
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>63218</id><entry>2023-04-20</entry><title>Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing</title><swanseaauthors><author><sid>81dc663ca0e68c60908d35b1d2ec3a9b</sid><ORCID>0000-0002-6618-7483</ORCID><firstname>Scott</firstname><surname>Yang</surname><name>Scott Yang</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-04-20</date><deptcode>SCS</deptcode><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 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.</abstract><type>Journal Article</type><journal>Neural Processing Letters</journal><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1370-4621</issnPrint><issnElectronic>1573-773X</issnElectronic><keywords>Spiking neural networks, Pair-based spiking-timing-dependent plasticity, integrate-and-fire neurons, stochastic computing</keywords><publishedDay>0</publishedDay><publishedMonth>0</publishedMonth><publishedYear>0</publishedYear><publishedDate>0001-01-01</publishedDate><doi>10.1007/s11063-023-11255-8</doi><url>http://dx.doi.org/10.1007/s11063-023-11255-8</url><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>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.</funders><projectreference/><lastEdited>2023-05-24T10:32:06.9016548</lastEdited><Created>2023-04-20T09:46:06.3018350</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Junxiu</firstname><surname>Liu</surname><order>1</order></author><author><firstname>Yanhu</firstname><surname>Wang</surname><order>2</order></author><author><firstname>Yuling</firstname><surname>Luo</surname><orcid>0000-0002-0117-4614</orcid><order>3</order></author><author><firstname>Shunsheng</firstname><surname>Zhang</surname><order>4</order></author><author><firstname>Dong</firstname><surname>Jiang</surname><order>5</order></author><author><firstname>Yifan</firstname><surname>Hua</surname><order>6</order></author><author><firstname>Sheng</firstname><surname>Qin</surname><order>7</order></author><author><firstname>Scott</firstname><surname>Yang</surname><orcid>0000-0002-6618-7483</orcid><order>8</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2023-05-03T09:27:16.1237172</uploaded><type>Output</type><contentLength>813938</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2024-04-06T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 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 SCS 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 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 0 0 0 0001-01-01 10.1007/s11063-023-11255-8 http://dx.doi.org/10.1007/s11063-023-11255-8 COLLEGE NANME Computer Science COLLEGE CODE SCS 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. 2023-05-24T10:32:06.9016548 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 Under embargo Under embargo 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
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
url http://dx.doi.org/10.1007/s11063-023-11255-8
document_store_str 0
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 0001-01-01T10:32:06Z
_version_ 1766767435209244672
score 11.012924

Similar Items