perfect case studies demonstrating order of magnitude ...dkw/papers/perfect... · automation...

PERFECTCaseStudiesDemonstratingOrderof

MagnitudeReductioninPowerConsumption

DavidK.Wittenberg,Edin Kadric,AndreDeHon,JonathanEdwards,JeffreySmith,andSilviu Chiricescu

TheProblem– WideAreaMotionImaging(WAMI)

• Wantreal-timedata• Highresolution- 368Cellphonecameras• 1.8Gpixels @10Hz• Airborne• Limitedbandwidthtoground• Limitedpower

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ARGUS-ISSystemComponents

ARGUS-ISsystemcomponents.Left:sensorin25”gimbal;Right:CFPAwith925MPixelFPAs.

ARGUS-ISruggedizedairborneprocessor

OurSolution

• LowerVoltage• Light-WeightChecks• ExploreParallelism• ContinuousHierarchyMemory

ARGUS-IScontextdrivesPERFECTenergyimprovements

©BAESystems2013

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Off−chipMemoryInterface

Reliable Coresand Infrastructure

Reliable Coresand InfrastructureLow Energy Array of Reconfigurable Nodes

Debayer

Lucas-

Kanade

Gaussia

nMixture

Mod

el

MeasureenergyonthePRACTICELEARNWAMIimplementation

MeasureenergyontheARGUS-ISWAMIpipelinecomponents

Rawimage Demosaicing Registration MotionDetectionandTracking FinalImage

4

6

Demosaicing Registration MotionDetectionandTracking

Debayer LK GMM WAMI

PEs 1 4 8 (4,4,4)

Ops/Cycle20 220 680 360

Area(cm2)0.0046 0.33 0.2 0.55

0.7VVdd Throughput(fps)540 150 1600 150

Efficiency(Gops/W)740 280 910 340

0.5VVdd Throughput(fps)110 30 320 50

Efficiency(Gops/W) 1500 560 1800 680

512×512Image

ParallelOptimizedDesign

ContinuousHierarchyMemory

Data

Addr[n-1]

Addr[n-2]Addr[n-3:0]

Lmseg(M/4)

Lmseg(M

/4)

Lmseg(M)

Lmseg(M

)

64k 128k 64k-CHM 128k-CHM 64k-CHM-S128k-CHM-S0

0.02

0.04

0.06

0.08

0.1

0.12

Lucas-Kanade Energy/Frame

memroutelogicclk

Memory Size

Ene

rgy(

J)

LightWeightChecks

• Example:Sortingvs.checkingthatitissorted• Notneededinmanyimagingalgorithms,astheyiterateuntilerrorsaresmallenough

Differentialvs.SingleVoltage(22nmexample)

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Vdd Vdd

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PERFECTResultsandComparisonwithARGUS

Technology Power (W) Time (s) Energy (J) GOps GOPS / WARGUS-ISGPU 77.4 6.5 507.2 940.6 1.9PERFECT 0.5 17.1 0.9 4096.6 439.9Ratio 0.01 2.6 0.002 4.4 236.9

• PERFECT Simulated Output for WAMI Pipeline– Scaled 512x512 pixel 5 Mpixel and further scaled to achieve 3000 frame run

• 7 nm process technology assumed as best-case– Varying Vdd and Mem-size led to optimized GOPS / W

• Vdd = 0.7, Mem size = 64k • Key Results:

– 237X GOPS/W increase even with memory intensive pipeline– Performed 4X more operations due to differences in implementation

Comparison of ARGUS-IS GPU and PERFECT output

TAILWINDSYSTEM PERFECTSYSTEM

ImageRegistration(SLAM)

MotionDetection(RFD)

ImageRegistration(LK)

MotionDetection(GMM)

STD CHM STD CHM

Energy/Frame(mJ) 752 237 175 129 19 14

Time/Frame(ms) 253 83 444 37

TAILWIND:PERFECTgives6-15XImprovement

• Motion Detection – PERFECT kernel consumes ≈ 15X less energy than the TAILWIND kernel• Image Registration – PERFECT kernel consumes ≈ 6X less energy than the TAILWIND kernel

1.ExceededPERFECTenergyefficiencygoalsbyupto10x(440-680Gops/W)oncommonsetofWideAreaMotionImagery(WAMI)kernels

2.TheprojectedArguspowerconsumptionreductionenables:• Airborne,real-time3Dsituationalawarenesstothewarfighteralongwith:– Miniaturizationof3Dmappingtechnologycurrentlyhousedinmultipleserverracks

– Increasedtheflightmissiontimeandtrackdetectionaccuracywithimproveddatacompressioninmannedflights

• Autonomousthreatdetection,trackingandmulti-sensorfusion3.DesignedanovelFPGAbasedarchitecturethatcombinesultralowpoweroperationwithhighreliability• Optimizethesubstrateanddesignmappingtominimizecommunicationenergy

Summary

AcronymsUsed

ARGUS AutonomousReal-timeGround-UbiquitousSurveillance-ImagingSystemCHM ContinuousHierarchyMemoryFIT FailuresInTimeFPGA FieldProgrammableGateArrayGMM GaussianMixtureModelLEARN Low-EnergyArchitectureofReconfigurableNodesL-K Lucas-KanadeLWC LightWeightChecksPERFECT PowerEfficiencyRevolutionforEmbeddedComputingTechnologyRANSAC RANdom SAmple ConsensusRFD RobustFrameDifferencesSLAM SimultaneousLocationAndMappingTAILWIND TacticalAircrafttoIncreaseLongWaveInfraredNighttimeDetectionUAV UnmannedAerialVehicleWAMI WideAreaMotionImagery

BACKUP

MinimalStreamingDesign

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FullyfunctionalpipelineDemosaicing Registration MotionDetectionandTracking

Debayer LK GMM WAMI

PEs 1 1 1 (1,1,1)

Ops/Cycle20 55 85 90

Area(cm2)0.0046 0.13 0.034 0.17

0.7VVdd

Throughput(fps)540 58 430 57

Efficiency(Gops/W)740 250 650 300

0.5VVdd

Throughput(fps)110 12 85 11

Efficiency(Gops/W) 1500 500 1300 600

512×512Image

WAMIonFPGA

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OptimizingParallelism

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LDeBayer LK GMM

512×512Image

64k 128k 64k-CHM 128k-CHM 64k-CHM-S128k-CHM-S0

0.005

0.01

0.015

0.02

0.025

GMM Energy/Frame

memroutelogicclk

Memory Size

Ene

rgy

(J)

Energyreductiontechniques*

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• Uselow-powertechnology• Voltagescaling• UseT-gatesorgate-boosting(GB)insteadofpass-Tw/levelrestorer• Usedual-Vdd• Powergating

*Edin Kadric “Energyreductionthroughvoltagescalingandlightweightchecking”,PhD.Thesis.

• TAILWIND – Fine-tuned

parameters for the different kernels19

TAILWINDSystemLevelMetricsDataSet SystemLevelMetric SLAM +RFD LK+RFD SLAM+GMM

QuanticoScene1 P(vehicle detection) 0.9 0.53 0.5

P(dismountdetection) 0.64 0.38 0.46

Falsealarm/minute 1.35 103 163

P(tracking vehicle) 0.3 0.1 0

P(trackingdismount) 0.33 0.14 0.14

QuanticoScene2 P(vehicledetection) 0.82 0.89 0.3



P(tracking vehicle) 0.42 0.25 0


QuanticoScene3 P(vehicledetection) 0.85 0.54 0.24



P(tracking vehicle) 0.15 0.13 0.03


• PERFECT– Dismounts are at granularity of a few pixels which is more than

the difference between consecutive frames – large false alarm rates

– GMM has lower levels of accuracy than RFD– Varying the GMM parameters (e.g. learning rate and # of

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ARGUS-ISPublicReleasePoster

ReferencesfromPERFECTproject1. EdinKadrik,DavidLakata andAndréDeHon.“ImpactofMemoryArchitectureonFPGAEnergyConsumption”,FPGA’15,

Monterey,California,USA,Feb22-24,2015,http://ic.ese.upenn.edu/pdf/meme_fpga2015.pdf.2. BenjaminGojman,Sirisha Nalmela,Nikil Mehta,NicholasHowarth,andAndréDeHon,“GROK-LAB:GeneratingRealOn-chip

KnowledgeforIntra-clusterDelaysusingTimingExtraction”,ACMTransactionsonReconfigurableTechnologyandSystems(TRETS),Volume7,Number4,DOI:10.1145/2597889,December,2014.

3. E.Kadric,K.Mahajan,A.DeHon,"KungFuDataEnergy—MinimizingCommunicationEnergyinFPGAComputations",IEEESymposiumonField-ProgrammableCustomComputingMachines(FCCM),May11–13,2014

4. E.Kadric,K.Mahajan,A.DeHon,"EnergyReductionthroughDifferentialReliabilityandLightweightChecking",FPGA’14,February26–28,2014,Monterey,California,USA.

5. FengLiu,Soumyadeep Ghosh,NickP.Johnson,DavidI.August.“CGPA:Coarse-GrainedPipelinedAccelerators”,DesignAutomationConference(DAC),201451stACM/EDAC/IEEE,1-5June2014,SanFrancisco,CA,http://dl.acm.org/citation.cfm?doid=2593069.2593105

6. AndreDeHon,“FundamentalUnderpinningsofReconfigurableComputingArchitectures”,acceptedforpublicationintheProceedingsoftheIEEE,Volume103,Issue:3,DOI:10.1109/JPROC.2014.2387696,March2015,http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7086421

7. FengLiu,Heejin Ahn,StephenR.Beard,Taewook Oh,andDavidI.August,“DynaSpAM:DynamicSpatialArchitectureMappingusingOutofOrderInstructionSchedules”,Proceedingsofthe42ndInternationalSymposiumonComputerArchitecture(ISCA),ISBN978-1-4503-3402-0/15/06,Portland,OR,USA,June13-17,2015.

8. EdinKadrik,DavidLakata andAndréDeHon,"ImpactofParallelismandMemoryArchitectureonFPGACommunicationEnergy",ACMTransactionsonReconfigurableTechnologyandSystems,Vol.9,tobepublisheddate:2016.0/15/06,Portland,OR,USA,June13-17,2015.

9. “DemonstrationofApplicabilityofPERFECT/PRACTICETechnologytoTAILWINDProcessing”,BAESystemsTechnicalReportforDARPAPERFECTProgram

10. “DemonstrationofApplicabilityofPERFECT-PRACTICETechnologytoARGUS-ISProcessing”,BAESystemsTechnicalReportforDARPAPERFECTProgram

perfect case studies demonstrating order of magnitude ...dkw/papers/perfect... · automation...

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