ApproximatingComputationandDataforEnergyEfficiency

1stIWESSeptember 20th,2016,Pisa,Italy

EDAGroupPolitecnico diTorino

Torino,Italy

DanieleJahierPagliari

2

Outline

§ ErrorToleranceandApproximateComputing

§OurView

§ ACinProcessing

§ ACinInterconnects

§ ACinActuators(OLEDDisplays)

§ Conclusions

3

ErrorTolerance§ Manyemergingapplicationsareerrortolerant (orresilient)

Applications Error

Tolerance

Noisy Inputs (e.g. sensor

data)

Multiple "Golden" Outputs

Human Perception

(e.g. multimedia)

Algorithm Features

Error!

NoisyInput NoisyOutput

OriginalImage Errorsin3LSBs

4

ApproximateComputing

Performance Demands

Energy Budget

• ”Smart”Systems• InternetofThings

• Battery-operated• Energy-autonomous

Tradeoffenergyconsumption andoutputquality leveragingapplicationserrortolerance.

ApproximateComputing(AC):

Software LevelProcessor Level

Architecture LevelLogic Level

AbstractionLevels:ESsDesignChallenges:

ClassicAC:• Design-timeapproximations(fixederror).RecentTrend:• Runtime-reconfigurableerror.

Issues:• Whataboutsystem-level?.• Whataboutautomation?

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Motivation§ EmbeddedSystem:

Environment

Processing

EETimes

Stanley-Marbell et al. DAC’16InSummary:§ Sensors,actuators andinterconnects are

relevantcontributorstoconsumption.§ Thebreakdownisstronglysystem-

dependent§ Approach:approximationasasystem-

leveldesignknob!

§ ESEnergyBreakdown:

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ACinProcessing:RPR

Idea:[Shanbag etal.ISLPED’99]§ VoltageOver-Scaling(VOS)onthe

originalcircuit(MDSP)§ ErrorControlBlock (EC-Block)to

mitigatetheeffectoftimingerrors.

Original Circuit(MDSP)

Error ControlIN

yM

y

Decision Block

Estimator(RPR)

yr

Arrivaltime[s]

N.ofp

aths

Tclk

Arrivaltime[s]

N.ofpaths

TclkVOS

Viol.

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ACinProcessing:RPR

Idea:[Shanbag etal.ISLPED’99]§ VoltageOver-Scaling(VOS)onthe

originalcircuit(MDSP)§ ErrorControlBlock (EC-Block)to

mitigatetheeffectoftimingerrors.

ECBlockStructure:§ Estimator oftheerror-freeoutput§ Decisionblock toselectbetween

MDSPandEstimatoroutputs

EstimatorImplementation:§ ReducedPrecisionReplica(RPR)

Original Circuit(MDSP)

Error ControlIN

yM

y

Decision Block

Estimator(RPR)

yr

Arrivaltime[s]

N.ofp

aths

Tclk

Arrivaltime[s]

N.ofpaths

TclkVOS

Viol.

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ACinProcessing:OurContributionClassicRPRhaslimitations:§ Replicadesignanderrorestimation

requireknowledgeoffunctionality(designspecific)

§ Usessimplifiedandunrealisticassumptions(e.g.oninputstatistics)

ProposedFramework:§ AutomaticallyaddRPRtoexisting

gate-levelnetlistofadatapathcircuit.

Features:§ Functionality-agnostic.§ Simulation-based.§ Integratedwithstate-of-the-art

tools forsynthesisandsimulation.

𝝈𝒔𝟐

𝝈𝒚𝒓𝟐 +𝝈𝜼𝟐

MDSPNetlist

RepresentativeInputSet

QualityEvaluation

01101110100111010001011000011011

RPRAutomation

Engine

RPRNetlist

EDATools(e.g.DC)

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ACinProcessing:ResultsSetup:§ 45nmlibraryfromSTM.§ Opencores designs,realisticquality

constraintsGenerality:§ SuccessfullyappliedRPRto

previouslyuntesteddesigns(CORDIC,SRU).

§ Comparablesavingsw.r.t.ad-hocapproach onFIRandFFT.

Benchmark Tot. Power Saving [%]

Area Ovr. [%]

FIR Filter 44.96 82.39FFT Butterfly 49.66 133.20RM-CORDIC 42.05 127.64SRU 47.91 143.32

Benefitsofsimulation-basedapproach:§ Differentinputstimulicausedifferent

errorrateontheMDSP,atthesameVVOS.§ Consequently,alarger/smallerreplicacan

beusedtoobtainthesamequality.§ Strongimpactofinputsonthe

obtainablepowersavings.

>20%difference!!

RPRpowersavingvsvoltageforaFIRfilter,withdifferentinputstimuli(samequalityconstraint).

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ACinInterconnects:MotivationSerialbuses:§ De-factostandardforinterfacingsensors,

actuatorsandI/Ocontrollers§ Higherfrequencies,nojitterissue,

reducedcrosstalk§ Lowercosts(lesspinsandeasierwiring)§ SPI,I2C,MIPI,etc.

Motivation:§ PCBtraceshavelargecapacitiveloadsthat

havenotscaledastransistors!§ Transmissionofone12bitsample »

executionof1instruction![1][2]

§ Tens ofserialconnectionsinasystem!

[1] P. Stanley-Marbell and M. Rinard. Value-deviation-bounded serial data encoding for energy-efficient approximate communication. 2015 [2] N. Ickes, et al. . A 10-pJ/instruction, 4-MIPS micropower DSP for sensor applications. 2008.

ErrorTolerantBusTraces:§ SensorICs/multimediaactuators(audio

DAC,displays)

§ Long“idle” (roughlyconstant)phases.§ Short“bursty”(fastandlargevariations)

phases.§ Example: Lena image(redchannel)

(Most)informationconveyedbythebursty phases!

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ACinInterconnects:ST0/ADETwoEncodingswithcommonPrinciples:§ Exploitidlephasesforpowersaving!§ Avoidredundancy(introduceslargeoverheadsinserialbuses)§ Allowruntime-reconfigurationofacceptederror.§ Simpleimplementation(CODECHWoverheadsmustnotoffsetgains).

§ SerialT0(ST0):§ Selectivelytransmitthecorrectdatumoraspecial0-Transitionspattern(interpretedas“repeatpreviousdatum").

§ |b(t)– b(t’)|> Thà Sendcorrectdata§ |b(t)– b(t’)|≤ Thà Send0-Tpattern.

§ ApproximateDifferentialEncoding(ADE):§ BasedonbitwiseDifferentialEncoding(DE):B(t)=b(t)⊕ b(t-1)§ EnhancedwithLSB-saturation toreducetransitionsalsoduringbursty phases

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ACinInterconnects:Results

0.05 0.1 0.15 0.2 0.25 0.3 0.35Error [%]

10

20

30

40

50

60

70

TC R

educ

tion

[%]

ADELSBSRAKEST0DE

0 0.1 0.2 0.3 0.4Error [%]

10

20

30

40

50

60

70

80

90

TC R

educ

tion

[%]

ADELSBSRAKEST0DE

1 2 3 4 5 6Error [%]

10

20

30

40

50

60

70

80

90

TC R

educ

tion

[%]

ADELSBSRAKEST0DE

Comparison:§ Rake [Stanley-Marbell,DAC’16]§ LSBS andAccurateDE

Results:§ ADEandST0arebothsuperiortostate-

of-the-art§ ST0betterfor“strongburstiness”,ADE

superiorformorerandomdata.

Images [Kodak Database] ECG [Physionet Database]

Accelerometer [PSR Database]

⋍+40%

⋍+18%

⋍+50%

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ACinActuators:OLEDDisplays

OLEDDisplays:§ Brighterandbetterviewingangles

w.r.t.LCDs§ Thinnerand/orflexiblepanels

OLEDsareemissive:§ Powerstronglydependsonpixels

luminanceand(secondarily)color

§ Poweroptimizationcanbeachievedwithanimagetransformation!(≠LCD)

Motivation:§ Transformationsforgeneralimagesmustpreservecontrastwhilereducingpowerconsumption.

§ Existingsolutionsarecomputationallyintensive.

§ Poweroverhead?§ Realtime applicability?

Claim:§ Similartransformationscanbeobtainedbymuchsimpler(approximate)computations.

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ACinActuators:OLEDDisplays

3rd OrderPolynomialFit:§ Transformimagesaccordingtoa3rd

orderpolynomialfunctionoftheinputluminance (YUVspace)

§ Polynomialevaluationvs.histogramprocessing,etc.

§ Simplerandfeweroperations(ADD,MULT)

Approach:1. OfflineTrainingPhase

(ComputationallyIntensive):

2. OnlineTransformation(LinearComplexity):

0 0.2 0.4 0.6 0.8 1Input Luminance (Y)

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Outpu

t Lum

inanc

e (Y t)

Data from Lee et al.3rd-order Polynomial Fit

Lee et al, TIP 2010

ImageDBExtraction offunction

parameters

QualityConstraints

Params.

Determine and apply image-

specific transformation

Params.

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ACinActuators:Results

§ Comparablequalityatiso-savingsw.r.t.state-of-the-art§ Visuallyandquantitatively:

§ Muchlowercomplexity!(SWorHW)§ 10xfasterthanLeeetal.§ Minimalpoweroverhead forHWimplementation.

Original Leeetal. Proposed

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ACinActuators:Results

§ Comparablequalityatiso-savingsw.r.t.state-of-the-art§ Visuallyandquantitatively:

§ Muchlowercomplexity!(SWorHW)§ 10xfasterthanLeeetal.§ Minimalpoweroverhead forHWimplementation.

Original Leeetal. Proposed

MSSIM0.692

MSSIM0.799

MSSIM0.860

MSSIM0.691

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ACinActuators:Results

§ Comparablequalityatiso-savingsw.r.t.state-of-the-art§ Visuallyandquantitatively:

§ Muchlowercomplexity!(SWorHW)§ 10xfasterthanLeeetal.§ Minimalpoweroverhead forHWimplementation.

Original Leeetal. Proposed

Saving61.6 %

Saving59.5 %

Saving55.1 %

Saving69.4 %

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Conclusions

§ Exploringtheenergyversusqualitytradeoffcanbeinterestingatsystemlevel:

§ Thecomputationpart isnotalwaystheonetoblame.

§ Automation aspectsarekeytothewidespreaddiffusionofthesedesigntechniques.

§ OpenIssues/FutureWork:§ ACinmemories?§ ACinsensors (and/or,ADCs)?§ Howto combineACtechniquesindifferentpartsofthesystemtomaximizetotalpowersavings?

§ (e.g.,encoding+RPR)

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THANKYOU!