基于STDP法則的全數(shù)字脈沖神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)

基于STDP法則的全數(shù)字脈沖神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)摘要：

全數(shù)字脈沖神經(jīng)網(wǎng)絡(luò)（DPNN）作為一種新型人工神經(jīng)網(wǎng)絡(luò)，在處理時(shí)空脈沖信息方面具有很大的優(yōu)勢(shì)。但是，有限精度的DPNN仍然受到系統(tǒng)噪聲、器件不匹配和非理想實(shí)現(xiàn)的影響，這些因素會(huì)導(dǎo)致DPNN在實(shí)際應(yīng)用中性能下降，因此需要探索更有效的設(shè)計(jì)方法。本文提出了一種基于SpikeTimingDependentPlasticity（STDP）法則的DPNN設(shè)計(jì)方法。首先，我們將STDP應(yīng)用于統(tǒng)一的網(wǎng)絡(luò)設(shè)計(jì)，包括永磁只讀存儲(chǔ)器（PMRM）、脈沖生成與重復(fù)模塊（PGRM）和喜馬拉雅級(jí)數(shù)模塊（HSRM）。其次，我們提出了一個(gè)新穎的思想：使用時(shí)序精度模擬浮點(diǎn)運(yùn)算。通過(guò)使用這種方法，我們能夠抵消系統(tǒng)噪聲和器件不匹配對(duì)精度的影響。最后，我們?cè)谑謱?xiě)數(shù)字識(shí)別任務(wù)上實(shí)現(xiàn)了DPNN的硬件實(shí)現(xiàn)，并與現(xiàn)有的實(shí)現(xiàn)進(jìn)行了比較。實(shí)驗(yàn)結(jié)果表明，我們的設(shè)計(jì)方法能夠顯著提高DPNN的性能和精度。

關(guān)鍵詞：

全數(shù)字脈沖神經(jīng)網(wǎng)絡(luò)，SpikeTimingDependentPlasticity，低精度數(shù)據(jù)表示

Abstract:

Asanoveltypeofartificialneuralnetwork,digitalpulseneuralnetwork(DPNN)hasgreatadvantagesindealingwithspatiotemporalpulseinformation.However,thelimitedprecisionDPNNisstillaffectedbysystemnoise,devicemismatchandnon-idealimplementation,whichwillleadtoperformancedegradationinpracticalapplications.Therefore,itisnecessarytoexploremoreeffectivedesignmethods.Inthispaper,aDPNNdesignmethodbasedontheSpikeTimingDependentPlasticity(STDP)ruleisproposed.First,weapplySTDPtotheunifiednetworkdesign,includingPermanentMagnetRead-OnlyMemory(PMRM),PulseGenerationandRepetitionModule(PGRM)andtheHimalayanSeriesModule(HSRM).Second,weproposeanovelidea:simulatingfloating-pointoperationswithtemporalaccuracy.Byusingthisapproach,wecanoffsettheimpactofsystemnoiseanddevicemismatchonaccuracy.Finally,weimplementedtheDPNNinhardwareforhandwrittendigitrecognitiontaskandcompareditwithexistingimplementations.TheexperimentalresultsshowthatourdesignmethodcansignificantlyimprovetheperformanceandaccuracyofDPNN.

Keywords:

Digitalpulseneuralnetwork,SpikeTimingDependentPlasticity,low-precisiondatarepresentationDigitalpulseneuralnetwork(DPNN)isatypeofartificialneuralnetwork(ANN)thatmimicsthebiologicalneurons'behavior.InDPNN,theinformationistransmittedusingdigitalpulses,whichisalsocalledspikes.ThespikingactivityismodeledbasedontheSpikeTimingDependentPlasticity(STDP),whichisabiologicallearningrule.TheSTDPupdateruleisbasedontheprecisetimingofpre-andpost-synapticspikes.Therefore,DPNNscanperformefficientcomputationsbyexploitingthetemporalinformationoftheinputs.

However,DPNNsalsofacesomechallenges,suchassystemnoiseanddevicemismatch,whichcandegradetheirperformanceandaccuracy.Inaddition,theconventionalapproachtorepresentdatainDPNNsuseshigh-precisionformats,whichincreasesthememoryrequirementandcomputationalcomplexityofthesystem.Therefore,thereisaneedtodeveloplow-precisiondatarepresentationtechniquesthatcanreducethememoryrequirementandpowerconsumptionandimprovetheperformanceofDPNNs.

Toaddressthesechallenges,researchershaveproposedadesignmethodforDPNNsthatoptimizesthenetworkparametersbasedonthespikingfrequencyoftheneurons.Themethodalsoincorporatesalow-precisiondatarepresentationscheme,whichencodestheinputdatausingfewerbits.TheproposedmethodcanimprovetheperformanceandaccuracyofDPNNswhilereducingthehardwarecost.

Intheproposedmethod,insteadofusinghigh-precisionweights,thenetworkweightsarerepresentedusinglow-precisionformats,suchasbinaryorternaryweights.Theinputdataisalsoquantizedbeforefeedingitintothenetwork.Theselow-precisionrepresentationscanreducethememoryaccessandcomputationrequiredfortheinferenceprocess.

Moreover,theproposedmethodincorporatesnoiseanddevicemismatchmodelsintothenetworkdesign.Themodelssimulatethesystemnoiseanddevicemismatch,whichcandegradethenetwork'sperformance.Byincorporatingthesemodels,thenetworkcanlearntooperatewithtemporalaccuracyandovercometheimpactofsystemnoiseanddevicemismatchonaccuracy.

Tovalidatetheproposedmethod,theresearchersimplementedtheDPNNinhardwareforthehandwrittendigitrecognitiontask.TheexperimentalresultsshowedthattheproposeddesignmethodsignificantlyimprovedtheperformanceandaccuracyofDPNNcomparedtoexistingimplementations.Theresultsalsoshowedthatthelow-precisiondatarepresentationschemeandthenoiseanddevicemismatchmodelscanreducethehardwarecostwhilemaintainingtheaccuracy.

Inconclusion,theproposeddesignmethodforDPNNscanimprovetheirperformanceandaccuracywhilereducingthehardwarecost.Themethodincorporateslow-precisiondatarepresentation,noise,anddevicemismatchmodelsintothenetworkdesigntooptimizethenetworkparametersbasedonthespikingfrequencyoftheneurons.TheexperimentalresultsshowedtheeffectivenessoftheproposedmethodforthehandwrittendigitrecognitiontaskFurthermore,theproposedmethodcanalsobeappliedtootherapplicationsthatrequirehighaccuracyandlowhardwarecost,suchasimageandspeechrecognition.Byutilizingtheproposedmethod,itispossibletodesignmoreefficientandaccurateDPNNsthatcanbeimplementedonlow-powerdevices,suchasmobilephones,smartwatches,andwearabledevices.

Inaddition,theproposedmethodcanalsobeextendedtoothertypesofneuralnetworks,suchasconvolutionalneuralnetworks(CNNs)andrecurrentneuralnetworks(RNNs),tofurtherimprovetheirperformanceandreducehardwarecost.Moreover,byintegratingtheproposedmethodwithotheroptimizationtechniques,suchaspruningandquantization,itispossibletodesignevenmoreefficientandaccurateneuralnetworks.

Finally,itisimportanttonotethatalthoughtheproposedmethodcansignificantlyreducethehardwarecostofDPNNs,itisnotapanaceaforallhardware-relatedissues.Thereareotherfactorsthatcanaffectthehardwarecost,suchasmemoryandprocessingspeed,whichshouldalsobetakenintoconsiderationwhendesigningneuralnetworksforlow-powerdevices.

Inconclusion,theproposedmethodfordesigningDPNNshasshownpromisingresultsinimprovingtheirperformanceandreducinghardwarecost.Itprovidesanewapproachfordesigningefficientandaccurateneuralnetworksthatcanbeimplementedonlow-powerdevices.Withfurtherresearchanddevelopment,theproposedmethodcanbeextendedtoothertypesofneuralnetworksandcanbecomeakeytoolforoptimizingneuralnetworkdesignOneareawheretheproposedmethodfordesigningDPNNscanbeparticularlyusefulisinedgecomputingapplications.Edgecomputinginvolvesperformingcomputationanddataprocessingclosertothesourceofthatdata,ratherthansendingalldatatoacentrallocationforprocessing.Thisisparticularlyimportantforapplicationsthatrelyonreal-timedataprocessing,suchasautonomousvehicles,drones,andsmarthomes.

However,edgedevicestypicallyhavelimitedcomputationalresourcesandbatterylife,whichmakesitdifficulttoefficientlyexecutecomplexmachinelearningmodelssuchasdeepneuralnetworks.TheproposedmethodcanhelpaddressthischallengebyenablingthedesignofDPNNsthatareoptimizedforlowpowerconsumption,whilestillprovidinghighaccuracy.Thiscanenablearangeofnewapplicationsthatrelyonedgecomputing,whilealsoreducingtheenvironmentalimpactofcompute-intensivetasks.

OnepotentialapplicationofDPNNsinlow-powerdevicesisinmachinevision.Forexample,wearabledevicessuchassmartglassescoulduseDPNNstoperformreal-timeobjectdetectionandrecognition,withoutrelyingonaconnectiontoamorepowerfuldeviceorinternetconnection.Thiscanenablenewapplicationssuchasenhancedaugmentedrealityexperiences,andobjectrecognitionforthevisuallyimpaired.

AnotherpotentialapplicationofDPNNsisinnaturallanguageprocessing(NLP)tasks,suchasspeechrecognitionandmachinetranslation.Thesetasksarecrucialformanyapplications,includingvirtualassistantsandlanguagetranslationapps.However,theycanbecomputationallyintensive,andmayrequireextensivepreprocessingandfeatureextraction.DPNNscanenablemoreefficientandaccurateNLPtasksonlow-powerdevices,enablingtheseapplicati