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1、IMPLEMENTATIONOFARTIFICIALNEURALNETWORKONFPGA12Dr.RezaRaeisi,ArminKabir1IndianaStateUniversity,Indiana;Email:reza@indstate.edu2IndianaStateUniversity,Indiana;Email:akabir1@indstate.edu1.IntroductionAnArtificialNeuralNetwork(ANN)isaninformationprocessingparadigm,whichisinspiredbytheway
2、biologicalnervoussystems,suchasthebrain,processinformation.Thekeyelementofthisparadigmisthenovelstructureoftheinformationprocessingsystem.Scientiststrytoimitatethebrain’scapabilitieswithANN(Haykin,1999).FPGAbasedreconfigurablecomputingarchitecturesarewellsuitedtoimplementANNbecausethe
3、parallelstructureofFPGAmatchesthetopologiesofANN.TheabilitytoeasilyreconfigureFPGAmakesthedesignlessexpensivethanpre-designedhardware,anditalsogivestheuserstheopportunitytochangetheweightsbasedontheirneeds(Ienne,1995).ImplementedANNonFPGAcanbeusedtoworkwithdifferentintegratedcircuits.
4、Forexample,ANNisusedincellphonestodecreasethenoise,inthisapplicationANNshouldbeimplementedonthehardwarebecauseofthesmallsizeofcellphonesandalsohighspeedthatisneededforthisapplication.AmericanSocietyforEngineeringEducationMarch31-April1,2006–IndianaUniversityPurdueUniversityFortWayne(I
5、PFW)2006Illinois-IndianaandNorthCentralJointSectionConference2.ArtificialNeuralNetworksSomeofthemostimportantpropertiesofthehumanbrainthatscientistsliketoimitatearetrial-and-errorandpatternlearning.WithANN,theyhavecomeastepclosertoreachingthisgoal.ANNisusedtosolvemanydailyproblemsindi
6、fferentareasofscience.SomeofthemanyapplicationsofANNareimageprocessing,signalprocessing,systemcontrol,anddatamining.TheabilityofANNtolearnbyexamplemakesitveryflexibleandpowerfulfornumerousapplications.NeuronsarethemainpartofeachANN.Eachneuroncanhavecountlessinputs;however,itwillonlyha
7、veoneoutputvalue,asillustratedinFigure2.1.X1..W1....F(?XiWi).....WnXnFigure2.1NeuronStructureTheoutputofeachneuroniscalculatedbythefollowingformula:Output=F(?Xi*Wi)Formula2.1WhereXiistheinputtotheneuron,andWiistheweightofeachinput.TheseweightsAmericanSocietyforEngineeringEducationMarc
8、h31-A
