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Evolutionary optimization of neural networks with heterogeneous computation: study and implementation

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Evolutionary optimization of neural networks with heterogeneous computation: study and implementation

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dc.contributor.author Fe, Jorge Deolindo es_ES
dc.contributor.author Aliaga Varea, Ramón José es_ES
dc.contributor.author Gadea Gironés, Rafael es_ES
dc.date.accessioned 2016-05-17T10:42:40Z
dc.date.available 2016-05-17T10:42:40Z
dc.date.issued 2015-08
dc.identifier.issn 0920-8542
dc.identifier.issn 1573-0484
dc.identifier.uri http://hdl.handle.net/10251/64230
dc.description.abstract In the optimization of artificial neural networks (ANNs) via evolutionary algorithms and the implementation of the necessary training for the objective function, there is often a trade-off between efficiency and flexibility. Pure software solutions on general-purpose processors tend to be slow because they do not take advantage of the inherent parallelism, whereas hardware realizations usually rely on optimizations that reduce the range of applicable network topologies, or they attempt to increase processing efficiency by means of low-precision data representation. This paper presents, first of all, a study that shows the need of heterogeneous platform (CPU–GPU–FPGA) to accelerate the optimization of ANNs using genetic algorithms and, secondly, an implementation of a platform based on embedded systems with hardware accelerators implemented in Field Pro-grammable Gate Array (FPGA). The implementation of the individuals on a remote low-cost Altera FPGA allowed us to obtain a 3x–4x acceleration compared with a 2.83 GHz Intel Xeon Quad-Core and 6x–7x compared with a 2.2 GHz AMD Opteron Quad-Core 2354. es_ES
dc.description.sponsorship The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain. en_EN
dc.language Inglés es_ES
dc.publisher Springer Netherlands es_ES
dc.relation.ispartof The Journal of Supercomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Evolutionary computation es_ES
dc.subject Embedded system es_ES
dc.subject FPGA es_ES
dc.subject Neural networks es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Evolutionary optimization of neural networks with heterogeneous computation: study and implementation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11227-015-1419-7
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Fe, JD.; Aliaga Varea, RJ.; Gadea Gironés, R. (2015). Evolutionary optimization of neural networks with heterogeneous computation: study and implementation. The Journal of Supercomputing. 71(8):2944-2962. doi:10.1007/s11227-015-1419-7 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s11227-015-1419-7 es_ES
dc.description.upvformatpinicio 2944 es_ES
dc.description.upvformatpfin 2962 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 71 es_ES
dc.description.issue 8 es_ES
dc.relation.senia 301857 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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