A 201.4 GOPS 496 mW Real-Time Multi-Object Recognition Proce(5)

A 201.4 GOPS real-time multi-object recognitionprocessor is presented with a three-stage pipelined architecture.Visual perception based multi-object recognition algorithm isapplied to give multiple attentions to multiple objects in the inputimage. For human-like multi-object perception, a neural perceptionengine is proposed with biologically inspired neural networksand fuzzy logic circ

36IEEEJOURNALOFSOLID-STATECIRCUITS,VOL.45,NO.1,JANUARY

2010

Fig.5.(a)Workload-awaretaskscheduling.(b)Applieddatabasesize

control.

Fig.6.BlockdiagramofneuralperceptionengineandSPUtask/powermanager.

proportionaltothenumberofinputdescriptorvectorsandthesizeofapplieddatabase.Basedonthese,theexecutiontimeoftheobjectdecisionstagecanbecontrolledbycon guringcoveragerateofdatabase.First,theSTMmeasuresthenumberofdescriptorvectorsfromtheSPUsandcalculatestheexpectedexecutiontimeofthevectormatching.Then,itcomparestheexpectedexecutiontimewiththetargetpipelinetimeandcon guresthedatabasecoveragerateoftheDPtomeetthepipelinetime.However,reducingcoveragerateshouldbecare-fullyperformedbecauseitcandegradetheoverallrecognitionrate.Witha16384-entrydatabasefor50objectsrecognition,correctlymatchedratedegrades0.6%and1.3%,whenthecoveragerateis0.95and0.90,respectively.WiththehelpoftheWATSandADSC,theexecutiontimesofthethreestagescanbebalancedtothetargetpipelinetime,16ms,evenunder

theworkloadvariations.Asaresult,theproposedprocessorachieves60frame/secframe-rateforVGA

(640480)sizedvideoinput.

IV.BUILDINGBLOCKDESIGN

A.NeuralPerceptionEngine

Fig.6showstheblockdiagramoftheNPE.Foref cientROIdetection,theNPEemploysa32-bitRISCcontrollerandthreehardwareengines;motionestimator(ME),visualattentionen-gine(VAE),andobjectdetectionengine(ODE).TheMEisem-ployedtoextractdynamicmotionvectorsbetweentwosequen-tialframesandimplementedbyarrayPEswithafullsearchblockmatchingmethod[13].TheVAEisemployedtoextract

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