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dc.creatorYousefzadeh, Amirrezaes
dc.creatorKhoei, Mina A.es
dc.creatorHosseini, Sahares
dc.creatorHolanda, Priscilaes
dc.creatorLeroux, Sames
dc.creatorMoreira, Orlandoes
dc.creatorTapson, Jonathanes
dc.creatorDhoedt, Bartes
dc.creatorSimoens, Pieteres
dc.creatorSerrano Gotarredona, María Teresaes
dc.creatorLinares Barranco, Bernabées
dc.date.accessioned2020-06-02T11:56:57Z
dc.date.available2020-06-02T11:56:57Z
dc.date.issued2019
dc.identifier.citationYousefzadeh, A., Khoei, M.A., Hosseini, S., Holanda, P., Leroux, S., Moreira, O.,...,Linares Barranco, B. (2019). Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9 (4), 668-678.
dc.identifier.issn2156-3357es
dc.identifier.issn2156-3365es
dc.identifier.urihttps://hdl.handle.net/11441/97361
dc.description.abstractInference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand- Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms.es
dc.description.sponsorshipEU H2020 grants 687299 ”NEURAM3”es
dc.description.sponsorshipEU H2020 grants 824164 ”HERMES”es
dc.description.sponsorshipMinistry of Economy and Competitivity (Spain) TEC2015- 63884-C2-1-P (COGNET)es
dc.formatapplication/pdfes
dc.format.extent11 p.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)es
dc.relation.ispartofIEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9 (4), 668-678.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAsynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsityes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.projectID687299 ”NEURAM3”es
dc.relation.projectID824164 ”HERMES”es
dc.relation.projectIDTEC2015- 63884-C2-1-P (COGNET)es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8890681es
dc.identifier.doi10.1109/JETCAS.2019.2951121es
dc.contributor.groupUniversidad de Sevilla. TIC178: Diseño y Test de Circuitos Integrados de Señal Mixtaes
idus.validador.notaPreprintes
dc.journaltitleIEEE Journal on Emerging and Selected Topics in Circuits and Systemses
dc.publication.volumen9es
dc.publication.issue4es
dc.publication.initialPage668es
dc.publication.endPage678es

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