Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based o...

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Autores: Aguirre, Fernando Leonel|||0000-0001-7793-1194, Pazos, Sebastián Matías|||0000-0002-7354-4530, Palumbo, Félix|||0000-0002-7749-5035, Suñé, Jordi|||0000-0003-0108-4907, Miranda, E.|||0000-0003-0470-5318
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:287865
Acceso en línea:https://ddd.uab.cat/record/287865
https://dx.doi.org/urn:doi:10.1109/ACCESS.2020.3035638
Access Level:acceso abierto
Palabra clave:RRAM
Resistive switching
Cross-point
Memory
Memristor
Neuromorphic
Pattern recognition
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spelling Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern RecognitionAguirre, Fernando Leonel|||0000-0001-7793-1194Pazos, Sebastián Matías|||0000-0002-7354-4530Palumbo, Félix|||0000-0002-7749-5035Suñé, Jordi|||0000-0003-0108-4907Miranda, E.|||0000-0003-0470-5318RRAMResistive switchingCross-pointMemoryMemristorNeuromorphicPattern recognitionWe investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities. 22020-01-0120202020-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/287865https://dx.doi.org/urn:doi:10.1109/ACCESS.2020.3035638reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 TEC2017-84321-C4-4-REuropean Commission https://doi.org/10.13039/501100000780 783176open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2878652026-06-06T12:50:31Z
dc.title.none.fl_str_mv Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
title Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
spellingShingle Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
Aguirre, Fernando Leonel|||0000-0001-7793-1194
RRAM
Resistive switching
Cross-point
Memory
Memristor
Neuromorphic
Pattern recognition
title_short Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
title_full Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
title_fullStr Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
title_full_unstemmed Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
title_sort Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
dc.creator.none.fl_str_mv Aguirre, Fernando Leonel|||0000-0001-7793-1194
Pazos, Sebastián Matías|||0000-0002-7354-4530
Palumbo, Félix|||0000-0002-7749-5035
Suñé, Jordi|||0000-0003-0108-4907
Miranda, E.|||0000-0003-0470-5318
author Aguirre, Fernando Leonel|||0000-0001-7793-1194
author_facet Aguirre, Fernando Leonel|||0000-0001-7793-1194
Pazos, Sebastián Matías|||0000-0002-7354-4530
Palumbo, Félix|||0000-0002-7749-5035
Suñé, Jordi|||0000-0003-0108-4907
Miranda, E.|||0000-0003-0470-5318
author_role author
author2 Pazos, Sebastián Matías|||0000-0002-7354-4530
Palumbo, Félix|||0000-0002-7749-5035
Suñé, Jordi|||0000-0003-0108-4907
Miranda, E.|||0000-0003-0470-5318
author2_role author
author
author
author
dc.subject.none.fl_str_mv RRAM
Resistive switching
Cross-point
Memory
Memristor
Neuromorphic
Pattern recognition
topic RRAM
Resistive switching
Cross-point
Memory
Memristor
Neuromorphic
Pattern recognition
description We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/287865
https://dx.doi.org/urn:doi:10.1109/ACCESS.2020.3035638
url https://ddd.uab.cat/record/287865
https://dx.doi.org/urn:doi:10.1109/ACCESS.2020.3035638
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 TEC2017-84321-C4-4-R
European Commission https://doi.org/10.13039/501100000780 783176
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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