A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

Nguyen, D-A; Tran, X-T; Iacopi, F

A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

Nguyen, D-A Tran, X-T Iacopi, F

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Journal of Low Power Electronics and Applications, 2021, 11, (2), pp. 23-23
Issue Date:: 2021-05-24

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Field	Value	Language
dc.contributor.author	Nguyen, D-A
dc.contributor.author	Tran, X-T
dc.contributor.author	Iacopi, F https://orcid.org/0000-0002-3196-0990
dc.date.accessioned	2021-07-01T23:47:20Z
dc.date.available	2021-07-01T23:47:20Z
dc.date.issued	2021-05-24
dc.identifier.citation	Journal of Low Power Electronics and Applications, 2021, 11, (2), pp. 23-23
dc.identifier.issn	2079-9268
dc.identifier.issn	2079-9268
dc.identifier.uri	http://hdl.handle.net/10453/149796
dc.description.abstract	<jats:p>Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Journal of Low Power Electronics and Applications
dc.relation.isbasedon	10.3390/jlpea11020023
dc.rights	Copyright:© 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Review of Algorithms and Hardware Implementations for Spiking Neural Networks
dc.type	Journal Article
utslib.citation.volume	11
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-07-01T23:47:18Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	11
utslib.citation.issue	2

Abstract:

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

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http://hdl.handle.net/10453/149796