dc.creator | Yousefzadeh, Amirreza | es |
dc.creator | Khoei, Mina A. | es |
dc.creator | Hosseini, Sahar | es |
dc.creator | Holanda, Priscila | es |
dc.creator | Leroux, Sam | es |
dc.creator | Moreira, Orlando | es |
dc.creator | Tapson, Jonathan | es |
dc.creator | Dhoedt, Bart | es |
dc.creator | Simoens, Pieter | es |
dc.creator | Serrano Gotarredona, María Teresa | es |
dc.creator | Linares Barranco, Bernabé | es |
dc.date.accessioned | 2020-10-15T09:36:06Z | |
dc.date.available | 2020-10-15T09:36:06Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Yousefzadeh, 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.issn | 2156-3357 | es |
dc.identifier.uri | https://hdl.handle.net/11441/101967 | |
dc.description.abstract | Inference 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.sponsorship | European Union's Horizon 2020 No 687299 NeuRAM | es |
dc.description.sponsorship | European Union's Horizon 2020 No 824164 HERMES | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2015-63884-C2-1-P | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9 (4), 668-678. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Spiking neural network | es |
dc.subject | Asynchronous Inference | es |
dc.subject | Temporal sparsity | es |
dc.subject | Deep Neural Network | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | Bio-inspired processing | es |
dc.subject | Neuromorphic hardware | es |
dc.title | Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | Horizon 2020 No 687299 NeuRAM | es |
dc.relation.projectID | Horizon 2020 No 824164 HERMES | es |
dc.relation.projectID | TEC2015-63884-C2-1-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8890681 | es |
dc.identifier.doi | 10.1109/JETCAS.2019.2951121 | es |
dc.journaltitle | IEEE Journal on Emerging and Selected Topics in Circuits and Systems | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 668 | es |
dc.publication.endPage | 678 | es |
dc.contributor.funder | European Union (UE) | es |
dc.contributor.funder | European Union (UE) | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |