title: Ultra-fast data-mining hardware architecture based on stochastic computing.
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.
authors.affiliation: Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
authors.forename: Antoni
authors.initials: A
authors.lastname: Morro
authors.affiliation: Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
authors.forename: Vincent
authors.initials: V
authors.lastname: Canals
authors.affiliation: Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
authors.forename: Antoni
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
authors.forename: Miquel L
authors.initials: ML
authors.lastname: Alomar
authors.affiliation: Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
authors.forename: Josep L
authors.initials: JL
authors.lastname: Rossello
mesh_terms: D003201:Computers; D057225:Data Mining; D019992:Databases as Topic; D006801:Humans; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors