Driving AI 2024 Mobileye 端到端 -2024-10-自動(dòng)駕駛

NavigatingThePathtoAutonomous

Mobility

Prof.AmnonShashua,CEO

Prof.ShaiShalev-Schwartz,CTO

HowtoSolveAutonomy

-Reachingareal“fullselfdriving”system(eyes-off)

-Whilemaintainingasustainablebusiness

*SubjecttodefinedOperationalDesignDomainandproductsspecifications

HowtoSolveAutonomy

Sensors

AIApproach

Cost

Modularity

GeographicScalability

MTBF

Waymo

Lidar-centric

CAIS

?

Tesla

Cameraonly

End-to-end

?

Mobileye

Camera-centric

CAIS

?

HowtoSolveAutonomy

Sensors

AIApproach

Cost

Modularity

GeographicScalability

MTBF

Waymo

Lidar-centric

CAIS

?

Tesla

Cameraonly

End-to-end

?

Mobileye

Camera-centric

CAIS

?

Whichismorelikely

tosucceed?

End-to-EndApproach

PremiseReality

Nogluecode

GluecodeshiftedtoofflineRare&correctvs.common&incorrect

“AValignment”problem

UnsuperviseddataalonecanreachsufficientMTBF

Really?

-Calculator

-Shortcutlearningproblem

-Longtailproblem

“NoGlueCode”:AVAlignmentProblem

End-to-endaimstomaximizeP[ylx]whereyisthefuturetrajectoryhumanwouldtake,denotedy,giventhepreviousvideo,denotedx

Thislearningobjectiveprefers'common&incorrect'over'rare&correct’

Examples:

1.Mostdriversslowdownatastopsignbutdonotcometoafullstop

-Rollingstop三common&incorrect

-Fullstop三rare&correct

2.“Rudedrivers”thatcutinline

3.Recklessdrivers

ThisiswhyRLHFisusedinLLMs:therewardmechanismdifferentiatesbetween‘correct’and‘incorrect’

Gluecodeshiftedtooffline

CanUnsupervisedDataAloneReachHighMTBF?

Calculators

End-to-endlearningfromdataoftenmissesimportantabstractionsandthereforedoesn’tgeneralizewell

Example

Learningtomultiply2numbers,ataskwhereeventhelargestLLMsstruggle

/yuntiandeng/status/1836114401213989366

CanUnsupervisedDataAloneReachHighMTBF?

Calculators

End-to-endlearningfromdataoftenmissesimportantabstractionsandthereforedoesn’tgeneralizewell

Example

Learningtomultiply2numbers,ataskwhereeventhelargestLLMsstruggle

Whatcanbedone?

ChatGPT

Callatool(calculator)

-ProvidetoolstoLLMs

-→CompoundAISystems(CAIS)

CanUnsupervisedDataAloneReachHighMTBF?

ShortcutLearningProblem

Relyingondifferentsensormodalitiesisawell-establishedmethodologyforincreasingMTBFThequestion:Howtofusethedifferentsensors?

The“end-to-endapproach”:Justfeedallsensorsintoonebignetworkandtrainit

“TheShortcutLearningProblem”

Whendifferentinputmodalitieshavedifferentsamplecomplexities,end-to-endStochasticGradientDescentstrugglesinleveragingtheadvantagesofallmodalities

CanUnsupervisedDataAloneReachHighMTBF?

ShortcutLearningProblemConsider3typesofsensors

Radar

Lidar

Camera

SupposethateachsystemhasinherentlimitationsthatcauseafailureprobabilityofE,whereEissmall(e.g.,onein1000hours)

Additionally,assumethatthefailuresofthedifferentsensorsareindependent

Wecomparetwooptions

-Lowlevel,end-to-end,fusion(trainasystembasedonthecombinedinput)

-CAIS:Decomposabletrainingofasystempereachmodality,followedbyhigh-levelfusion

Whichoptionisbetter?

ShortcutLearningProblem:ASimpleSyntheticExample

Distribution:allvariablesareover{+1,-1},anddataiscreatedbythefollowingsimplegenerativemodel:

y-B(),r1,r2,r3-i·i·d.B,x1=yr1x2=yr2x4x5-i·i·d.B()x3=yr3x4x5

ThisisasimplemodeloffusionbetweenLidar,Radar,Camerasystemswiththefollowingproperties:

-The3systemshaveuncorrelatederrors(modeledbyr1,r2,r3)oflevele

-x1andx2are”simpler”systems(modelingradarandlidar),whiletheproductofx3x4x5equalstoyr3,andthereforeisa“complicatedtolearn”system(modelingthecamera)

Theorem:

-CaneasilyreacherrorofO(e2)withdecomposabletrainingof1-hidden-layerFCN+majority

-End-to-endSGDtrainingwillbe“stuck”atanerrorofeforT/ewhereTisthetimecomplexityoflearningthecomplicatedsystem(camera)individually

Whathappened?Isn’tend-to-endalwaysbetter?

Shortcutlearningproblem:End-to-endSGDstrugglestoleveragesystemswithdifferentsamplecomplexities

CanUnsupervisedDataAloneReachHighMTBF?

TheLongTailProblem

Intheoptimisticscenario,afewrareeventsreducetheprobabilitymassconsiderablyInthepessimisticscenario,eachrareeventhasminimalimpactontheprobabilitymass

P(event)

PessimisticScenario

ToomanyrareeventswhereeachdoesnotreduceP(event)noticeably

OptimalScenario

Events

LongTailofTeslaFSD

-TeslaFSDtrackerindicatesthatreducingvariancesolelythroughadatapipelineresultsinincrementalprogress

/news/735038/tesla-fsd-occasionally-dangerously-inept-independent-test/

*-publicdataonTesla'srecent12.5.x

HowtoSolveAutonomy

Sensors

AIApproach

Cost

Modularity

GeographicScalability

MTBF

Waymo

Lidar-centric

CAIS

?

Tesla

Cameraonly

End-to-end

?

Mobileye

Camera-centric

CAIS

?

TheBias-VarianceTradeoffinMachineLearning

Bias(‘a(chǎn)pproximationerror’)

Totalerror

ThelearningsystemcannotreflectthefullrichnessofrealityVariance(‘generalizationerror’)

Thelearningsystemoverfitstotheobserveddata,andfailstogeneralizetounseenexamples

Error

ε

VarianceBias

Totalerror

AbstractionInjections

MobileyeCompoundAISystem(CAIS)

AVAlignment

RSS

Separatescorrectfromincorrect

ReachingSufficientMTBF

Abstractions

-Sense/Plan/Act

-Analyticcalculations:RSS,time-to-contact…

Redundancies

Sensors

Algo

Highlevelfusion

MobileyeCompoundAISystem(CAIS)

AVAlignment

RSS

Separatescorrectfromincorrect

ReachingSufficientMTBF

Abstractions

-Act

ExtremelyEfficientAI

(Shaiwillcover)

Sense/Plan/

-Analyticcalculations:RSS,time-to-contact…

Redundancies

Highlevelfusion

Algo

Sensors

PGF

HighLevelFusion:HowtoPerform

Considerasimplecase

Wearefollowingaleadvehicle,andwehave3sensors

Camera

Radar

Lidar

Iftherearecontradictionsbetweenthesensors,wheresomedictateastrongbrakingwhileothersnot,whatshouldwedo?

Majority:2outof3(2oo3)Propertyofmajority

IfeachmodalityhasanerrorprobabilityofatmostE,andtheerrorsareindependent,then-majorityvotehasanerrorprobabilityofo(e2)

MajorityisNotAlwaysApplicable

Nowconsider3systems,eachonepredictswhereisourlane

Majorityisnotdefinedfornon-binarydecisions,sowhatcanbedone?

ThePrimary-Guardian-Fallback(PGF)Fusion

Weproposeageneralapproachforgeneralizingthemajorityruletononbinarydecisions

Webuild3systems

-Primary(P)-Predictswherethelaneis

-Guardian(G)-Checksifthepredictionoftheprimarysystemisvalidornot

-Fallback(F)-Predictswherethelaneis

Fusion:

-IfGuardiandictatesPrimaryisvalid,choosevalid

-Otherwise,chooseFallback

Theorem:ThePGFhasthesamepropertyofthemajorityrule

IfthefailureprobabilityofeachsystemisatmostEandtheseprobabilitiesareindependent,thenthefusedsystemhasanerrorofo(E2)

MobileyeCompoundAISystem(CAIS)

AVAlignment

RSS

Separatescorrectfromincorrect

ExtremelyEfficientAI

ReachingSufficientMTBF

Abstractions

-Act

Sense/Plan/

-Analyticcalculations:RSS,time-to-contact…

Redundancies

Sensors

Algo

Highlevelfusion

ExtremelyEfficientAI

TransformersforSensingandPlanningatx100efficiency

Inferencechip(EyeQ6H):Designforefficiency

EfficientlabelingbyAutoGroundTruth

Efficientmodularitybyteacher-studentarchitecture

Prologue

6AIRevolutions

MachineLearning

DeepLearning

GenerativeAI

UniversalLearning

Transformers

Sim2Real

Reasoning

Pre-Transformers:ObjectDetectionPipeline

Clusteringandmax

suppression

2Dto3D

ThreeRevolutionsof

GenerativePretrainedTransformers(GPTs)

Tokenizeeverything

Generative,Auto-regressive

Transformerarchitecture:’Attentionisallyouneed’

ThreeRevolutionsofGenerativePretrainedTransformers

Tokenizeeverything

Input:Transcribeeachinputmodality(e.g.,text,images)intoasequenceoftokens

Output:Transcribeeachoutputmodalityasasequenceoftokensandemploygenerative,auto-regressivemodelswithsuitablelossfunction

Accommodates:Complexinputandoutputstructures(e.g.,sets,sequences,trees)

Objectdetectionpipelineexample:

Input

Singleimage

’Tokenized’input

Sequenceofimagepatches

‘Tokenized’output

Sequenceof4coordinatesdeterminingthelocationoftheobjectsintheimage

ThreeRevolutionsofGenerativePretrainedTransformers

Generative,Auto-regressive

Previousapproach:Classificationorregressionwithfixed,smallsize,outputs(e.g.,ImageNet)Currentapproach:Learnprobabilitiesforsequencesofarbitrarylength(e.g.,sentence

generation)

KeyFeatures:ChainRule-Modelssequencedependencies

Generative-FitsdatausingmaximumlikelihoodEnables:Self-supervision(e.g.,futurewordsinadocument)

Handlesuncertainty(multiplevalidoutputsbylearningP[ylx])

ThreeRevolutionsofGenerativePretrainedTransformers

Example:Considera1000x1000pixelimagecontaining4vehicles,withtheimagedividedinto10x10pixelpatches.Whataretheprobabilitiesforidentifyingvehiclepositionswhennotusingthechainrulecomparedtowhenusingthechainrule?

x1,1,y1,1,X1,2,y1,21……,X4,1,y4,1,X4,2,y4,2

Listof4coordinatespervehicle

Usingthechainrule

PVehiclesII

=Px1)1II*Py1)1Ix1)1)I*…*Py4)2Ix1)1)…)x4)2)I

Dim=100

Withoutusingthechainrule

PVehiclesII=Px1)1)y1)1)x1)2)y1)2)….)x4)1)y4)1)x4)2)y4)2IIDim=1032

ThreeRevolutionsofGenerativePretrainedTransformers

Transformerarchitecture:’Attentionisallyouneed’

TailoredforproblemofpredictingPtokenn+1tokennstokenn1,tokenol]

...

Transformerlayern

Self-reflection

Self-attention

FCNFCNFCNFCNFCNFCNFCN

...

...

TransformersLayer:GroupThinkingAnalogy

Imagineateamdiscussingaproject

-Eachpersonhastheirownareaofexpertise

-theyallcontributetotheoveralloutcome

-Everyoneisworkingsimultaneouslyratherthanoneafteranother

Self-attention

Eachmemberlistenstoothersandrespondsin

real-time,adjustingtheirinputbasedon

importantpointsraised

Somethingis

fullyblocking

myview,maybe

atruck

Doesanyoneseea

closetruckonour

leftside?

Ipartiallysawaverybigwheel

Ihavenoidea

No

Self-reflection

Eachparticipanttakestimealonetoprocessideasandorganizetheirthoughts

TransformersLayer:Self-Reflection

-Eachtokenindividuallyprocessesits‘knowledge’usingamulti-layer-perceptron,withoutinteractingwithothertokens

n

Input

}d

Self-reflection

Self-reflection

FCN

...

FCN

...

d2n

...

Self-reflection

FCN

...

Output

TransformersLayer:Self-Attention

-Eachtokensend(query’totheothertokens,whichrespondwithvaluesiftheir(key’matchthe(query’

-Thequeryingtokenthenaveragesthereceivedvalues,facilitatinginter-tokenconnectivity

...

Questions

QueryKeyValueQueryKeyValueQueryKeyValue

ExamplefromtheGroupThinkingAnalogy

Personiasks:“Doesanyoneknowssomethingaboutx?”

Personjresponds:“Yes,Ihavewhattosayaboutit”

Personj′responds:”No,Idon’tknowanythingaboutit”

Relevancy

...

...

queryikeyj

..

i,j..

.

.

n2d

...

...

...

No,Idon’t

knowanything

aboutit

Yes,Ihave

whattosay

aboutit

Doesanyone

knowsomething

aboutx?

TransformersLayer:Self-Attention

NormalizesScores:Itconvertsrawattentionscoresintonormalized

probabilities

ProbabilityDistribution:Eachsetofattentionscoresistransformed

sothattheirprobabilitiessumto1

FocusMechanism:Thisallowsthemodeltoweighdifferentpartsof

theinputdifferently,focusingmoreon

relevantpartsbasedontheprobabilities

...

...

...

i,j

...

...

...

...

Normalize

eachrowby

SoftMax

...

Messageigetsfromthegroup

...

aijvj

j

...

...

αi,j

...

...

...

Indicateshowmuchi

wantstopayattentiontoj

Transformers:Complexity

L*(nd2+n2d)

#layers

Self

reflection

Self

attention

Costperlayerforalternativearchitectures:

FullyConnectedNetwork(FCN)Flattenndvalues

RecurrentNeuralNetwork(RNN)‘Talks’onlywithprevioustoken

...

...

Input

...

...

...

...

...

...

...

...

Output

...

Connections:d2n2

Connections:nd2

Transformers

‘EffectiveSparsity’ofTransformers

Sparserd2n+n2d

Anymodality

ConvolutionalNeural

FullyConnectedNetwork(FCN)

Networks(CNNs)

d2n2ConnectionsSparsityspecifictoimages

Denser,buteffectivelyselects

onlyafewpasttokensfor

communication

Long-Short-Term-Memory

(LSTM)

RecurrentNeuralNetworks

(RNN)

Markovsparsitycontext

representedbyastatevector

The3RevolutionsEnableaUniversalSolution

Handlealltypesofinputs

Dealswithuncertainty(bylearningprobability)Enablesalltypesofoutputs

Theultimatelearningmachine?

ATransformerEnd-to-endObjectDetectionNetwork

Input:images

Output:allobjects

ATransformerEnd-to-endObjectDetectionNetwork

The5“Multi”problems

Multi-camera:surround

Multi-frame::frommultipletimestamps

Multi-object:needstooutputall(vehicles,pedestrians,hazards,…)

Multi-scale:needstodetectfarandcloseobjectsatdifferentresolutions

Multi-lanes:needstoassignobjectstorelevantlanes/crosswalks

-UniversalityofTransformers

-Encodeimagepatches(fromdifferentcameras,differentframes,anddifferentresolutions)astokens

-Encodeobjectsasasequenceoftokens(foreachobject:position,velocity,dimensions,type)

-ApplyaTransformertogeneratetheprobabilityofoutputtokensgiveninputtokensinanAuto-Regressivemanner

NetworkArchitecture:VanillaTransformer

-CNNbackboneforcreatingimagetokens:

-C=32highresolutionimagesareconvertedto32imagesofresolution20x15yieldingNp=300"pixels))perimage,andd=256channels

-Encoder:

-WehaveN=C*Np=9600uimagetokens)),eachatdimensiond=256

-AvanillatransformernetworkwithLlayersrequireso(L*N2d+d2N)

-Encoderalonerequiresaround100TOPs(assuming10Hz,L=32)

-Decoder:

-Predictasequenceoftokensrepresentingalltheobjects(hundredsoftokens)

-AvanillaARdecodingissequential,andwithKVcache,eachiterationinvolvescomputeofatleasto(LNd)pertokenprediction(buttherealissueisIOofLNdhere)

-Around100Mbpertokenprediction!

VanillaTransformersareNotEffiecient

Transformersareabruteforceapproachwithlimitedwaytoutilizepriorknowledge

Thisisthe“darkside”ofuniversality

Self-connectivity:nd2Inter-connectivity:n2d

n2d

InAVn≈104,whichbecomesabottleneck

GPT3

d=12288n=2048

nd2=317B

Wepayboth

-Samplecomplexity(dislargeasitneedstohandlealltheinformationineachtoken)

-Computationalcomplexityofinference(n,darelarge)

-(bothissuesareknownintheliterature,andgeneralmitigationssuchas“mixture-of-experts”and“state-space-models”wereproposed)

WhatAboutEnd-to-EndFromPixelstoControlCommands

Weaknessesoftransformers

Bruteforce

Thelearningobjective(oflearningpylx])prefers‘common&incorrect’yover'rare&correct’y

QuestionablewhetheritcanreachsufficientlyhighMTBF

-Missesimportantabstractionsandthereforedoesn’tgeneralizewell

-TheShortcutLearningProblem

(aspartofCAIS,oure2earchitecturehasanadditionalheadthatoutputscontrolcommandsdirectlyaswell,whichisfineasalowMTBFredundantcomponent)

MobileyeCompoundAISystem(CAIS)

AVAlignment

RSS

Separatescorrectfromincorrect

ReachingSufficientMTBF

Abstractions

-Sense/Plan/Act

-Analyticcalculations:RSS,time-to-contact…

Redundancies

Sensors

Algo

Highlevelfusion

Implications

-MustoutputSensingState

-Eachsubsystemmustbesuperefficientbecausewedon’thaveasinglesystem

ExtremelyEfficientAI

TransformersforSensingandPlanningatx100efficiency

EfficientlabelingbyAutoGroundTruth

STAT:SparseTypedAttention

Vanillatransformer:n2d+d2n

STAT:

-TokenTypes:Eachtokenhasa“type”

-Dimensionality:ofembeddingsandself-reflectionmatricesmayvarybasedonthetokentype.

-TokenConnectivity:Theconnectivitybetweentokensissparseanddependsontheirtypes

-LinkTokens:Weadd“l(fā)ink”tokensforcontrollingtheconnectivity

-InferenceEfficiency:Forourend-to-endobjectdetectiontask,STATisx100fasteratinferencetimeandatthesametimeslightlyimprovesperformance

STAT:SparseTypedAttention

Vanillatransformer:n2d+d2n

STATEncoderforObjectDetection:

-Tokentypes:

-Imagetokens:recall,wehaveC=32imageseachwithNp=300“pixels”,yielding9600imagetokens

-WeaddNL=32“Link”tokensperimage

-STATBlock:

-Withineachimage,CrossAttentionbetweenthe300imagetokensandthe32linktokens(C?Np?NL?d)

-Acrossimages,fullselfattentionbetweenalllinktokensC?NL2d

2

-ComparedtoC?Npdinvanillatransformers,wegetafactorimprovementof,whichisapproximatelyx100fasterinourcase

-Performance:Forourend-to-endobjectdetectiontask,STATisnotonlyx100,butalsoimprovesperformance(weenlargetheexpressivityofthenetworkwhilemakingitmuchfasteratinferencetime)

...

300imagetokens

...

32Linktokens

...

300imagetokens

...

32Linktokens

...

C=32images

...

300imagetokens

...

32Linktokens

Crossattention

300imagetokens

...

...

32Linktokens

...

300imagetokens

...

32Linktokens

...

300imagetokens

...

32Linktokens

...

Crossimage

300imagetokens

...

32Linktokens

...

ParallelAuto-Regressive(PAR)

Weneedtodetectallobjectsinthescene:Whatistheorder?Auto-Regressive:Itdoesn’tmatterduetothechainrule!

Priceofsequentialdecoding

-Sequentialdecodingiscostlyonallmoderndeeplearningchips(duetoIO)

-Weaddedun-needed”fakeuncertainty”(whatistheorder)

”Truckandtrailer”problem

DeTR(DETectionTransformer,FacebookAI,May2020)

-Outputallobjectsinparallel

-Hungarianmatchingtodeterminetherelativeorderbetweenthenetwork’spredictionsandtheorderofthegroundtruth

-Problem:Doesn’tdealwellwithtrueuncertainty

-The“truckandtrailer”problem

-Streetswhichcanbe1or2lanes,etc.

Parisstreets

ParallelAuto-Regressive(PAR)

-Thedecodercontainsqueryheadswhich

performcrossattentionwiththeencoder’slinktokensentirelyinparallel

-Eachqueryheadoutputs,auto-regressively,

0/1/2objects(independentlyandinparalleltotheotherqueryheads)

-→dealingonlywith“trueuncertainties”andnotwith“fakeuncertainties”

Inputimages

CNNTokenization

STATEncoder

Outputtokens

Queryheads

IntermediateSummary

MachineLearning

TransformersrevolutionizedAI

-Thegood

-Universal,generative,AI

DeepLearning

-Thebad

Transformers

-Can’tseparate“correct&rare”from“wrong&common”

-Missimportantabstractions

GenerativeAI

-Questionablewhenveryhighaccuracyisrequired

-Theugly

-Bruteforceapproach,unnecessarilyexpensive

UniversalLearning

Workingsmarterwithtransformers

Sim2Real

-STAT:x100faster&betteraccuracy

-PAR:x10faster&embraceuncertaintyonlywhenitisneeded

Reasoning

ExtremelyEfficientAI

efficiency

Inferencechip(EyeQ6H):Designforefficiency

EfficientlabelingbyAutoGroundTruth

LowHigh

efficiencyEfficiencyefficiency

HardwareArchitecturesTradeoff:Flexibilityvs.Efficiency

●Fixed-function

GPU

.CPU

SpecialpurposeFlexibilityGeneralpurpose

EyeQ6High:5DistinctArchitectures

EyeQ6H

LowHigh

efficiencyEfficiencyefficiency

XNN

-AddressMobileye’shighefficiencyandflexibilityneeds

PMA

-Enableacceleratingrangeofparallelcomputeparadigms

VMP

MPC

MIPS

SpecialpurposeFlexibilityGeneralpurpose

5DistinctArchitectures:EnhancedParallelProcessing

●MIPS

-Ageneral-purposeCPU

MPC

-ACPUspecializedforthreadlevelparallelism

●VMP

-Very-Long-Instruction-Width(VLIW)-Single-Instruction-Multiple-Data(SIMD)

-Designedfordata-levelparallelismoffixedpointsarithmetic(e.g.,convergethe12-bitrawimageintoasetof8-bitimagesofdifferentresolutionsandtone-maps)

-Basically,performsoperationsonvectorsofintegers

●PMA

-Coarse-Grain-Reconfigurable-Array(CGRA)

-Designedfordata-levelparallelismincludingfloatingpointarithmetic

-Basically,performsoperationsonvectorsoffloats

●XNN

-Dedicatedtofixedfunctionsfordeeplearning:convolutions,matrix-multiplication/fully-connect,andrelatedactivationpost-processingcomputations:ExcelsinCNNs,FCNs,Transformers

EyeQ5H

EyeQ6H

EyeQ6Hvs.EyeQ5H:2xinTOPS,But10xinFPS!

1200

1000

Framesper

Second

800

600

400

200

0

16TOPS(int8)

27W(max)

34TOPS(int8)

33W(max)

1151

1062

975

252

EyeQ6H

126

82

25

EyeQ5H

91

WeightedAverage

PixelLabelingRoadMultiObject

Detection

NeuralNetwork

EyeQ6Hvs.Orin:It’sNotAllAboutTOPS

TheoreticalTOPS

34TOPS(int8)