A 201.4 GOPS 496 mW Real-Time Multi-Object Recognition Proce(12)

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

KIMetal.:A201.4GOPS496mWREAL-TIMEMULTI-OBJECTRECOGNITIONPROCESSORWITHBIO-INSPIREDNEURALPERCEPTIONENGINE

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Fig.16.(a)GOPS/Wcomparison.(b)Energy/frame

comparison.

Fig.17.Demonstrationsystem.

TABLEICHIPS

UMMARY

TABLEII

POWERBREAK-D

OWN

processorachieves8.2mJenergydissipationperframeforVGAsizedvideoinput,whichis3.2timeslowerthanthebestofthepreviousobjectrecognitionprocessor.

Forthevalidationofthefabricatedchip,ademonstrationsystemforreal-timeobjectrecognitionisdevelopedasshowninFig.17.Itiscomposedoftargetobjects,videocamcorder,evaluationboard,andLCDdisplay.Theevaluationboardiscomposedofthree oors,whichareforhostprocessor,videodecoderandfabricatedrecognitionchip,andperipheralinterfacessuchasLCDdisplay,serial,USB,andEthernet,respectively.Inthedemonstrationsystem,thefabricatedchipisusedasavisionprocessingacceleratorwhilethehostprocessorcontrolsthewholeprogramsequencesandaccessesperipheralmodulestodisplaytheresultsandtointerfacewiththeexternaldevices.Theoverallobjectrecognitionisperformedbythreesteps.First,theinputimageofthetargetobjectsiscapturedfromthevideocamcorderanddecodedtothree-channelRGBpixeldatabythevideodecoder.Then,http://www.77cn.com.cnst,the nalrecognitionresultsaredisplayedwiththekey-pointsattheLCDscreenbythehostprocessor.

VIII.CONCLUSION

Inthiswork,wehaveproposedareal-timemulti-objectrecognitionprocessorwithathree-stagepipelinedarchitec-ture.Thevisualperceptionbasedmulti-objectrecognitionalgorithmhasbeendevelopedtogivemultipleattentionstomultipleobjectsintheinputimage.Forhuman-likemulti-ob-jectperception,aneuralperceptionenginehasbeenproposedwithbiologicallyinspiredneuralnetworksandfuzzylogic

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