dc.contributor.author | Morro, Antoni | |
dc.contributor.author | Canals, Vincent | |
dc.contributor.author | Oliver, Antoni | |
dc.contributor.author | Alomar Barceló, Miquel L. | |
dc.contributor.author | Rosselló, Josep L. | |
dc.date.accessioned | 2015-07-06T10:16:38Z | |
dc.date.available | 2015-07-06T10:16:38Z | |
dc.date.issued | 2015-05-08 | |
dc.identifier.citation | 1932-6203 | |
dc.identifier.uri | http://hdl.handle.net/11201/1164 | |
dc.description.abstract | Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories. We design pulse-based stochastic-logic blocks to obtain an efficient pattern recognition system. The proposed architecture speeds up the screening process of huge databases by a factor of 7 when compared to a conventional digital implementation using the same hardware area. | |
dc.language.iso | eng | |
dc.publisher | Public Library of Science | |
dc.relation.isformatof | Reproducció del document publicat a: http://dx.doi.org/10.1371/journal.pone.0124176 | |
dc.relation.ispartof | Plos One, 2015 | |
dc.rights | cc-by (c) Morro, Antoni et al., 2015 | |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es | |
dc.title | Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/publishedVersion | |
dc.date.updated | 2015-07-06T10:16:39Z | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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