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Article: Reservoir Computing using Diffusive Memristors

TitleReservoir Computing using Diffusive Memristors
Authors
KeywordsDiffusive memristors
Drift memristors
Modified national institute of standards and technology
Readout layers
Reservoir computing
Issue Date2019
PublisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/26404567
Citation
Advanced Intelligent Systems, 2019, v. 1 n. 7, article no. 1900084 How to Cite?
AbstractReservoir computing (RC) is a framework that can extract features from a temporal input into a higher‐dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. RC systems inherently exhibit an advantage, since the training is only performed at the readout layer, and therefore they are able to compute complicated temporal data with a low training cost. Herein, a physical reservoir computing system using diffusive memristor‐based reservoir and drift memristor‐based readout layer is experimentally implemented. The rich nonlinear dynamic behavior exhibited by a diffusive memristor due to Ag migration and the robust in situ training of drift memristor arrays makes the combined system ideal for temporal pattern classification. It is then demonstrated experimentally that the RC system can successfully identify handwritten digits from the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 83%.
Persistent Identifierhttp://hdl.handle.net/10722/291115
ISSN
2023 Impact Factor: 6.8
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMidya, R-
dc.contributor.authorWang, Z-
dc.contributor.authorAsapu, S-
dc.contributor.authorZhang, X-
dc.contributor.authorRao, M-
dc.contributor.authorSong, W-
dc.contributor.authorZhuo, Y-
dc.contributor.authorUpadhyay, N-
dc.contributor.authorXia, Q-
dc.contributor.authorYang, JJ-
dc.date.accessioned2020-11-04T08:28:43Z-
dc.date.available2020-11-04T08:28:43Z-
dc.date.issued2019-
dc.identifier.citationAdvanced Intelligent Systems, 2019, v. 1 n. 7, article no. 1900084-
dc.identifier.issn2640-4567-
dc.identifier.urihttp://hdl.handle.net/10722/291115-
dc.description.abstractReservoir computing (RC) is a framework that can extract features from a temporal input into a higher‐dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. RC systems inherently exhibit an advantage, since the training is only performed at the readout layer, and therefore they are able to compute complicated temporal data with a low training cost. Herein, a physical reservoir computing system using diffusive memristor‐based reservoir and drift memristor‐based readout layer is experimentally implemented. The rich nonlinear dynamic behavior exhibited by a diffusive memristor due to Ag migration and the robust in situ training of drift memristor arrays makes the combined system ideal for temporal pattern classification. It is then demonstrated experimentally that the RC system can successfully identify handwritten digits from the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 83%.-
dc.languageeng-
dc.publisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/26404567-
dc.relation.ispartofAdvanced Intelligent Systems-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDiffusive memristors-
dc.subjectDrift memristors-
dc.subjectModified national institute of standards and technology-
dc.subjectReadout layers-
dc.subjectReservoir computing-
dc.titleReservoir Computing using Diffusive Memristors-
dc.typeArticle-
dc.identifier.emailWang, Z: zrwang@hku.hk-
dc.identifier.authorityWang, Z=rp02714-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1002/aisy.201900084-
dc.identifier.hkuros700003890-
dc.identifier.volume1-
dc.identifier.issue7-
dc.identifier.spagearticle no. 1900084-
dc.identifier.epagearticle no. 1900084-
dc.identifier.isiWOS:000675636200008-
dc.publisher.placeGermany-
dc.identifier.issnl2640-4567-

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