File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Learning with Resistive Switching Neural Networks

TitleLearning with Resistive Switching Neural Networks
Authors
Rao, MingyiXia, QiangfeiYang, J. JoshuaWang, ZhongruiLi, CanJiang, HaoMidya, RivuLin, PengBelkin, DanielSong, WenhaoAsapu, Shiva
Issue Date2019
Citation
Technical Digest - International Electron Devices Meeting, IEDM, 2019, v. 2019-December How to Cite?
DOI: http://dx.doi.org/10.1109/IEDM19573.2019.8993465
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
0163-1918
2023 SCImago Journal Rankings: 1.047
ISI Accession Number ID
WOS:000553550000035

 

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-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats