Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applicatio...

ver descrição completa

Detalhes bibliográficos
Autores: Camuñas Mesa, Luis Alejandro, Linares Barranco, Bernabé, Serrano Gotarredona, María Teresa
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/98922
Acesso em linha:https://hdl.handle.net/11441/98922
https://doi.org/10.3390/ma12172745
Access Level:acceso abierto
Palavra-chave:Neuromorphic systems
Spiking neural networks
Memristors
Spike-timing-dependent plasticity
id ES_575d592ae6df31fe29b8f695239de5dc
oai_identifier_str oai:idus.us.es:11441/98922
network_acronym_str ES
network_name_str España
repository_id_str
spelling Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware ImplementationsCamuñas Mesa, Luis AlejandroLinares Barranco, BernabéSerrano Gotarredona, María TeresaNeuromorphic systemsSpiking neural networksMemristorsSpike-timing-dependent plasticityInspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.EU H2020 grant 687299 ”NEURAM3”EU H2020 grant 824164 ”HERMES”Ministry of Economy and Competitivity (Spain) and European Regional Development Fund TEC2015-63884-C2-1-P (COGNET)VI PPIT through the Universidad de Sevilla.MDPIArquitectura y Tecnología de ComputadoresTIC178: Diseño y Test de Circuitos Integrados de Señal Mixta2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/98922https://doi.org/10.3390/ma12172745reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMaterials, 12 (17), 2745-.687299 ”NEURAM3”824164 ”HERMES”TEC2015-63884-C2-1-P (COGNET)https://www.mdpi.com/1996-1944/12/17/2745info:eu-repo/semantics/openAccessoai:idus.us.es:11441/989222026-06-17T12:51:07Z
dc.title.none.fl_str_mv Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
title Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
spellingShingle Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
Camuñas Mesa, Luis Alejandro
Neuromorphic systems
Spiking neural networks
Memristors
Spike-timing-dependent plasticity
title_short Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
title_full Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
title_fullStr Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
title_full_unstemmed Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
title_sort Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
dc.creator.none.fl_str_mv Camuñas Mesa, Luis Alejandro
Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author Camuñas Mesa, Luis Alejandro
author_facet Camuñas Mesa, Luis Alejandro
Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author_role author
author2 Linares Barranco, Bernabé
Serrano Gotarredona, María Teresa
author2_role author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TIC178: Diseño y Test de Circuitos Integrados de Señal Mixta
dc.subject.none.fl_str_mv Neuromorphic systems
Spiking neural networks
Memristors
Spike-timing-dependent plasticity
topic Neuromorphic systems
Spiking neural networks
Memristors
Spike-timing-dependent plasticity
description Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/98922
https://doi.org/10.3390/ma12172745
url https://hdl.handle.net/11441/98922
https://doi.org/10.3390/ma12172745
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Materials, 12 (17), 2745-.
687299 ”NEURAM3”
824164 ”HERMES”
TEC2015-63884-C2-1-P (COGNET)
https://www.mdpi.com/1996-1944/12/17/2745
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408446950932480
score 15.198674

Registos relacionados