Plasticity in memristive devices for spiking neural networks

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation...

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Autores: Saïghi, Sylvain, Mayr, Christian G., Serrano-Gotarredona, Teresa, Schmidt, Heidemarie, Lecerf, Gwendal, Tomas, Jean, Grollier, Julie, Boyn, Sören, Vincent, Adrien F., Querlioz, Damien, La Barbera, Selina, Alibart, Fabien, Vuillaume, Dominique, Bichler, Olivier, Gamrat, Christian, Linares-Barranco, Bernabé
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/114596
Acceso en línea:http://hdl.handle.net/10261/114596
Access Level:acceso abierto
Palabra clave:Memristive device
Memristor
Neuromorphic engineering
Plasticity
Hardware neural network
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spelling Plasticity in memristive devices for spiking neural networksSaïghi, SylvainMayr, Christian G.Serrano-Gotarredona, TeresaSchmidt, HeidemarieLecerf, GwendalTomas, JeanGrollier, JulieBoyn, SörenVincent, Adrien F.Querlioz, DamienLa Barbera, SelinaAlibart, FabienVuillaume, DominiqueBichler, OlivierGamrat, ChristianLinares-Barranco, BernabéMemristive deviceMemristorNeuromorphic engineeringPlasticityHardware neural networkMemristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal usePeer reviewedFrontiers MediaEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2015info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/114596reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/granAgreement/EC/FP7/mework Programme (FP7/nº. 269459http://dx.doi.org/10.3389/fnins.2015.00051Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1145962026-05-22T06:33:51Z
dc.title.none.fl_str_mv Plasticity in memristive devices for spiking neural networks
title Plasticity in memristive devices for spiking neural networks
spellingShingle Plasticity in memristive devices for spiking neural networks
Saïghi, Sylvain
Memristive device
Memristor
Neuromorphic engineering
Plasticity
Hardware neural network
title_short Plasticity in memristive devices for spiking neural networks
title_full Plasticity in memristive devices for spiking neural networks
title_fullStr Plasticity in memristive devices for spiking neural networks
title_full_unstemmed Plasticity in memristive devices for spiking neural networks
title_sort Plasticity in memristive devices for spiking neural networks
dc.creator.none.fl_str_mv Saïghi, Sylvain
Mayr, Christian G.
Serrano-Gotarredona, Teresa
Schmidt, Heidemarie
Lecerf, Gwendal
Tomas, Jean
Grollier, Julie
Boyn, Sören
Vincent, Adrien F.
Querlioz, Damien
La Barbera, Selina
Alibart, Fabien
Vuillaume, Dominique
Bichler, Olivier
Gamrat, Christian
Linares-Barranco, Bernabé
author Saïghi, Sylvain
author_facet Saïghi, Sylvain
Mayr, Christian G.
Serrano-Gotarredona, Teresa
Schmidt, Heidemarie
Lecerf, Gwendal
Tomas, Jean
Grollier, Julie
Boyn, Sören
Vincent, Adrien F.
Querlioz, Damien
La Barbera, Selina
Alibart, Fabien
Vuillaume, Dominique
Bichler, Olivier
Gamrat, Christian
Linares-Barranco, Bernabé
author_role author
author2 Mayr, Christian G.
Serrano-Gotarredona, Teresa
Schmidt, Heidemarie
Lecerf, Gwendal
Tomas, Jean
Grollier, Julie
Boyn, Sören
Vincent, Adrien F.
Querlioz, Damien
La Barbera, Selina
Alibart, Fabien
Vuillaume, Dominique
Bichler, Olivier
Gamrat, Christian
Linares-Barranco, Bernabé
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Memristive device
Memristor
Neuromorphic engineering
Plasticity
Hardware neural network
topic Memristive device
Memristor
Neuromorphic engineering
Plasticity
Hardware neural network
description Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/114596
url http://hdl.handle.net/10261/114596
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/granAgreement/EC/FP7/mework Programme (FP7/nº. 269459
http://dx.doi.org/10.3389/fnins.2015.00051
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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