Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognition

Autores
Aguirre, Fernando Leonel; Pazos, Sebastián Matías; Palumbo, Felix Roberto Mario; Suñé, Jordi; Miranda, Enrique
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Aguirre, Fernando Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Universitat Autònoma de Barcelona; España
Fil: Pazos, Sebastián Matías. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina
Fil: Palumbo, Felix Roberto Mario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina
Fil: Suñé, Jordi. Universitat Autònoma de Barcelona; España
Fil: Miranda, Enrique. Universitat Autònoma de Barcelona; España
Materia
CROSS-POINT
MEMORY
MEMRISTOR
NEUROMORPHIC
PATTERN RECOGNITION
RESISTIVE SWITCHING
RRAM
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/163235
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognitionAguirre, Fernando LeonelPazos, Sebastián MatíasPalumbo, Felix Roberto MarioSuñé, JordiMiranda, EnriqueCROSS-POINTMEMORYMEMRISTORNEUROMORPHICPATTERN RECOGNITIONRESISTIVE SWITCHINGRRAMhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We 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.Fil: Aguirre, Fernando Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Universitat Autònoma de Barcelona; EspañaFil: Pazos, Sebastián Matías. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; ArgentinaFil: Palumbo, Felix Roberto Mario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; ArgentinaFil: Suñé, Jordi. Universitat Autònoma de Barcelona; EspañaFil: Miranda, Enrique. Universitat Autònoma de Barcelona; EspañaInstitute of Electrical and Electronics Engineers2019-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/163235Aguirre, Fernando Leonel; Pazos, Sebastián Matías; Palumbo, Felix Roberto Mario; Suñé, Jordi; Miranda, Enrique; Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognition; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 11-2019; 202174-2021932169-3536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9248999/info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2020.3035638info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:06:02Zoai:ri.conicet.gov.ar:11336/163235instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:06:03.143CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
CROSS-POINT
MEMORY
MEMRISTOR
NEUROMORPHIC
PATTERN RECOGNITION
RESISTIVE SWITCHING
RRAM
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
Pazos, Sebastián Matías
Palumbo, Felix Roberto Mario
Suñé, Jordi
Miranda, Enrique
author Aguirre, Fernando Leonel
author_facet Aguirre, Fernando Leonel
Pazos, Sebastián Matías
Palumbo, Felix Roberto Mario
Suñé, Jordi
Miranda, Enrique
author_role author
author2 Pazos, Sebastián Matías
Palumbo, Felix Roberto Mario
Suñé, Jordi
Miranda, Enrique
author2_role author
author
author
author
dc.subject.none.fl_str_mv CROSS-POINT
MEMORY
MEMRISTOR
NEUROMORPHIC
PATTERN RECOGNITION
RESISTIVE SWITCHING
RRAM
topic CROSS-POINT
MEMORY
MEMRISTOR
NEUROMORPHIC
PATTERN RECOGNITION
RESISTIVE SWITCHING
RRAM
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv 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.
Fil: Aguirre, Fernando Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Universitat Autònoma de Barcelona; España
Fil: Pazos, Sebastián Matías. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina
Fil: Palumbo, Felix Roberto Mario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina
Fil: Suñé, Jordi. Universitat Autònoma de Barcelona; España
Fil: Miranda, Enrique. Universitat Autònoma de Barcelona; España
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 2019
dc.date.none.fl_str_mv 2019-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/163235
Aguirre, Fernando Leonel; Pazos, Sebastián Matías; Palumbo, Felix Roberto Mario; Suñé, Jordi; Miranda, Enrique; Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognition; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 11-2019; 202174-202193
2169-3536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/163235
identifier_str_mv Aguirre, Fernando Leonel; Pazos, Sebastián Matías; Palumbo, Felix Roberto Mario; Suñé, Jordi; Miranda, Enrique; Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognition; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 11-2019; 202174-202193
2169-3536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9248999/
info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2020.3035638
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842980240475815936
score 12.993085

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