Towards memristive crossbar-based neuromorphic HW accelerators for signal processing

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dc.contributor.authorVourkas, I.
dc.contributor.authorAbusleme Hoffman, Ángel Christian
dc.contributor.authorVasileiadis, N.
dc.contributor.authorSirakoulis, G. C.
dc.contributor.authorPapamarkos, N.
dc.date.accessioned2022-05-13T19:15:19Z
dc.date.available2022-05-13T19:15:19Z
dc.date.issued2017
dc.description.abstractResearch progress in neuromorphic hardware, capable of biological perception and cognitive information processing, is leading the way towards a revolution in computing technology. Current research efforts have focused mainly on resistive switching devices, the electronic analog of synapses in artificial neural networks (ANNs), and the crossbar nanoarchitecture, for its huge connectivity and maximum integration density. In this context, this work presents the design and simulation of a memristive crossbar-based ANN for text recognition tasks, implementing a novel computing algorithm. In such case study, important issues during the application mapping process are identified and properly addressed at device and circuit level. The computing capabilities of the proposed system are highlighted through SPICE-level circuit simulations, which show excellent agreement with theoretical simulation results.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/MOCAST.2017.7937678
dc.identifier.isbn978-1509043866
dc.identifier.urihttps://doi.org/10.1109/MOCAST.2017.7937678
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7937678
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63886
dc.information.autorucEscuela de ingeniería ; Abusleme Hoffman, Ángel Christian ; S/I ; 2698
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofInternational Conference on Modern Circuits and Systems Technologies (6° : 2017 : Thessaloniki, Grecia)
dc.rightsacceso restringido
dc.subjectNeurons
dc.subjectMemristors
dc.subjectNeuromorphics
dc.subjectText recognition
dc.subjectSimulation
dc.subjectIntegrated circuit modeling
dc.subjectComputer architecture
dc.titleTowards memristive crossbar-based neuromorphic HW accelerators for signal processinges_ES
dc.typecomunicación de congreso
sipa.codpersvinculados2698
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