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1、>REPLACETHISLINEWITHYOURPAPERIDENTIFICATIONNUMBER(DOUBLE-CLICKHERETOEDIT)<1Usingdigitalmaskstoenhancethebandwidthtoleranceandimproveperformanceinon-chipreservoircomputingsystemsBendixSchneider,JoniDambre,Member,IEEEandPeterBienstman,Member,IEEEonconventionalcomputers.Aspecialroleisattributedtoa
2、ll-Abstract—ReservoirComputing(RC)isacomputingschemeopticalRCset-upsduetotheirinherenthighbandwidthwhichrelatedtorecurrentneuralnetworktheory.Asamodelforallowsforatremendousincreaseincomputingspeed.Aneuralactivityinthebrainitattractsalotofattention,especiallynetworkofintegratedSOAs(semiconductor
3、opticalbecauseofitsverysimpletrainingmethod.However,buildingaamplifiers)[14],[15],asinglenon-linearnodeinadelayedfunctional,on-chip,photonicimplementationofRCremainsafeedbackloop[16],andasemiconductorlaserdiodesubjecttochallenge.ScalingdelaylinesdownfromopticalfibrescaletochipscaleresultsinRCsys
4、temsthatcomputefaster,butatthedelayedself-feedback[17]havebeenproposedamongothers.sametimerequirethattheinputsignalsarealsoscaledupinTheon-chipphotonicintegrationofareservoiroffersthespeed,whichmightbeimpracticalorexpensive.Inthispaper,weadvantageofminiaturisation,cost-effectivenessandtheshowtha
5、tthisproblemcanbealleviatedbyamaskedRCsystempossibilityformassfabrication.However,itcanonlyperforminwhichtheamplitudeoftheinputsignalismodulatedbyausefulcomputationifitsinternaltimescalesmatchthebinary-valuedmask.Foraspeechrecognitiontaskwetimescaleoftheinputsignalathand.Itisdesirabletopreserved
6、emonstratethatthenecessaryinputsampleratecanbeafactorof40smallerthaninaconventionalRCsystem.Additionally,wetheinherentlyhighbandwidththatisofferedbythephotonicalsoshowthatlineardiscriminantanalysisandinputmatriximplementation.Yet,thischoicecompelstimescaleoptimisationisawell-performingalternativ
7、etolinearregressioncompressionformanyreal-timeapplications,e.g.speechforreservoirtraining.recognition,andimpliestheuseofhigh-speed,analoguewaveformgeneratorsasprerequisitesforRC.GiventhattheseIndexTerms—Photonicreservoircomp