dc.creator | Yousefzadeh, Amirreza | es |
dc.creator | Hosseini, Sahar | es |
dc.creator | Holanda, Priscila | es |
dc.creator | Leroux, Sam | es |
dc.creator | Werner, Thilo | es |
dc.creator | Serrano Gotarredona, María Teresa | es |
dc.creator | Linares Barranco, Bernabé | es |
dc.creator | Dhoedt, Bart | es |
dc.creator | Simoens, Pieter | es |
dc.date.accessioned | 2020-10-19T05:58:03Z | |
dc.date.available | 2020-10-19T05:58:03Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Yousefzadeh, A., Hosseini, S., Holanda, P., Leroux, S., Werner, T., Serrano Gotarredona, M.T.,...,Simoens, P. (2019). Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization. En AICAS 2019: IEEE International Conference on Artificial Intelligence Circuits and Systems (81-85), Hsinchu, Taiwan: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-5386-7884-8 | es |
dc.identifier.uri | https://hdl.handle.net/11441/102028 | |
dc.description.abstract | Artificial Neural Networks (ANNs) show great performance
in several data analysis tasks including visual and auditory
applications. However, direct implementation of these algorithms without
considering the sparsity of data requires high processing power,
consume vast amounts of energy and suffer from scalability issues.
Inspired by biology, one of the methods which can reduce power
consumption and allow scalability in the implementation of neural
networks is asynchronous processing and communication by means
of action potentials, so-called spikes. In this work, we use the wellknown
sigma-delta quantization method and introduce an easy and
straightforward solution to convert an Artificial Neural Network to a
Spiking Neural Network which can be implemented asynchronously
in a neuromorphic platform. Briefly, we used asynchronous spikes to
communicate the quantized output activations of the neurons. Despite
the fact that our proposed mechanism is simple and applicable to
a wide range of different ANNs, it outperforms the state-of-the-art
implementations from the accuracy and energy consumption point of
view. All source code for this project is available upon request for the
academic purpose | es |
dc.description.sponsorship | European Union's Horizon 2020 No 687299 NeuRAM | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2015-63884-C2-1-P | es |
dc.format | application/pdf | es |
dc.format.extent | 5 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | AICAS 2019: IEEE International Conference on Artificial Intelligence Circuits and Systems (2019), p 81-85 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization | es |
dc.type | info:eu-repo/semantics/conferenceObject | 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 | TEC2015-63884-C2-1-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8771624 | es |
dc.identifier.doi | 10.1109/AICAS.2019.8771624 | es |
dc.publication.initialPage | 81 | es |
dc.publication.endPage | 85 | es |
dc.eventtitle | AICAS 2019: IEEE International Conference on Artificial Intelligence Circuits and Systems | es |
dc.eventinstitution | Hsinchu, Taiwan | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | European Union (UE) | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |