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Conference Paper: Learning with Resistive Switching Neural Networks

TitleLearning with Resistive Switching Neural Networks
Authors
Issue Date2019
Citation
Technical Digest - International Electron Devices Meeting, IEDM, 2019, v. 2019-December How to Cite?
Abstract© 2019 IEEE. With the slowdown of Moore's law and the intensification of memory wall as well as von-Neumann bottleneck, processing-in-memory with emerging non-volatile analog devices, such as RRAMs or memristors, is a potential solution to accelerate machine learning in hardware neural networks, which may drastically improve the energy-area efficiency. In this paper, we discuss three major types of learning, namely the supervised, reinforcement, and unsupervised learning that are implemented with various 1-transistor-1-memristor (1T1R) based neural networks.
Persistent Identifierhttp://hdl.handle.net/10722/287022
ISSN
2023 SCImago Journal Rankings: 1.047
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorRao, Mingyi-
dc.contributor.authorXia, Qiangfei-
dc.contributor.authorYang, J. Joshua-
dc.contributor.authorWang, Zhongrui-
dc.contributor.authorLi, Can-
dc.contributor.authorJiang, Hao-
dc.contributor.authorMidya, Rivu-
dc.contributor.authorLin, Peng-
dc.contributor.authorBelkin, Daniel-
dc.contributor.authorSong, Wenhao-
dc.contributor.authorAsapu, Shiva-
dc.date.accessioned2020-09-07T11:46:17Z-
dc.date.available2020-09-07T11:46:17Z-
dc.date.issued2019-
dc.identifier.citationTechnical Digest - International Electron Devices Meeting, IEDM, 2019, v. 2019-December-
dc.identifier.issn0163-1918-
dc.identifier.urihttp://hdl.handle.net/10722/287022-
dc.description.abstract© 2019 IEEE. With the slowdown of Moore's law and the intensification of memory wall as well as von-Neumann bottleneck, processing-in-memory with emerging non-volatile analog devices, such as RRAMs or memristors, is a potential solution to accelerate machine learning in hardware neural networks, which may drastically improve the energy-area efficiency. In this paper, we discuss three major types of learning, namely the supervised, reinforcement, and unsupervised learning that are implemented with various 1-transistor-1-memristor (1T1R) based neural networks.-
dc.languageeng-
dc.relation.ispartofTechnical Digest - International Electron Devices Meeting, IEDM-
dc.titleLearning with Resistive Switching Neural Networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IEDM19573.2019.8993465-
dc.identifier.scopuseid_2-s2.0-85081060493-
dc.identifier.volume2019-December-
dc.identifier.spagenull-
dc.identifier.epagenull-
dc.identifier.isiWOS:000553550000035-
dc.identifier.issnl0163-1918-

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