使用深度學(xué)習(xí):人工智能簡化現(xiàn)實(shí)世界問題

Dr.

Ettikan

Kandasamy

KaruppiahDirector,

Developers’

Ecosystem2

November2017REAL

WORLD

PROBLEMSIMPLIFICATIONUSING

DEEP

LEARNING

/

AI“Find

where

I

parkedmy

car”AI

ISEVERYWHERE“Find

the

bag

I

just

sawin

thismagazine”“What

movie

shouldI

watch

next?”Bringing

grandmother

closer

tofamily

by

bridging

language

barrierTOUCHING

OUR

LIVESPredicting

sick

baby’s

vitals

like

heartrate,

blood

pressure,

survival

rateEnabling

the

blind

to

“see”

theirsurrounding,

read

emotions

on

facesIncreasing

public

safety

with

smartvideo

surveillance

at

airports

&

mallsAI

FORPUBLICGOODProviding

intelligent

servicesin

hotels,

banks

andstoresSeparating

weeds

as

itharvests,reduces

chemical

usage

by

90%5HOW

A

DEEP

NEURAL

NETWORK

SEESRaw

dataLow-level

featuresMid-level

featuresHigh-level

featuresKNOWYOURPROBLEM

WELL3000AIMOMENTUMTODAYBY

2020AIstartups85%$47B20%of

allcustomerspend

on

AIof

companies

willservice

interactions

willtechnologiesdedicate

workers

tobe

powered

bymonitor

and

guideAI

botsneural

networksEVERY

INDUSTRY

HAS

AWOKEN

TO

AIOrganizations

engaged

withNVIDIA

on

DeepLearning201420161,54919,439Higher

EdInternetHealthcareFinanceAutomotiveGovernmentOthersDeveloper

Tools9GPU-Computing

perf1.5X

per

year1000Xby2025RISE

OF

GPUCOMPUTING105104103102107106Single-threaded

perf1980

1990

2000

2010

2020Original

data

up

to

theyear

2010

collectedand

plotted

by

M.

Horowitz,

F.

Labonte,O.

Shacham,

K.

Olukotun,

L.

Hammond,

and

C.

Batten

New

plot

and

data

collectedfor

2010-2015

by

K.

Rupp1.5X

per

year1.1X

per

yearAPPLICATIONSSYSTEMSALGORITHMSCUDAARCHITECTURE10CPU

GPUAdd

GPUs:

Accelerate

Data

Processing

&

Analytics?

NVIDIA

2013NVIDIA

IGNITESTHE

AI

BIG

BANGArtificial

intelligence

is

the

use

of

computers

to

simulate

human

intelligence.AI

amplifies

our

cognitive

abilities

—

letting

us

solve

problems

where

thecomplexity

is

too

great,

the

information

is

incomplete,

or

the

details

aretoo

subtle

and

require

expert

training.Learning

from

data

—

a

computer’s

version

of

life

experience

—

is

how

AI

evolves.GPU

computing

powers

the

computation

required

for

deep

neural

networks

to

learnto

recognize

patterns

from

massive

amounts

of

data.This

new

computing

model

sparked

the

AI

era.DEEP

LEARNING

FRAMEWORKSVISIONSPEECHBEHAVIORImage

Classification

Object

DetectionVoiceRecognition LanguageTranslationRecommendationEnginesSentiment

AnalysisDEEP

LEARNINGcuDNNMATH

LIBRARIEScuBLAS

cuSPARSE

cuFFTMULTI-GPUNCCLMocha.jlNVIDIA

DEEP

LEARNING

SDKHigh

PerformanceGPU-Acceleration

for

DeepLearningANNOUNCING

TESLA

V100GIANT

LEAP

FORAI

&

HPCVOLTA

WITH

NEW

TENSORCORE21Bxtors

| TSMC

12nm

FFN

|

815mm25,120

CUDAcores7.5

FP64

TFLOPS

| 15

FP32

TFLOPSNEW

120

Tensor

TFLOPS20MB

SM

RF

| 16MBCache16GB

HBM2@

900

GB/s300

GB/sNVLinkNEW

TENSOR

CORENew

CUDA

TensorOp

instructions&

data

formats4x4

matrix

processing

arrayD[FP32]

=

A[FP16]

*

B[FP16]

+

C[FP32]Optimized

for

deep

learningActivation

InputsWeights

InputsOutput

Results1516MODEL

COMPLEXITY

IS

EXPLODING2016

—

Baidu

Deep

Speech

22015—

Microsoft

ResNet2017

—

Google

NMT105

ExaFLOPS8.7

Billion

Parameters20ExaFLOPS300

Million

Parameters7

ExaFLOPS60

Million

ParametersREVOLUTIONARY

AI

PERFORMANCE3X

Faster

DL

Training

PerformanceOver

80x

DL

TrainingPerformance

in

3

Years1x

K808x

P100cuDNN64x

M40cuDNN340x20x60xQ2170x

cuDNN2Q1

Q3

Q215

15

16Googlenet

TrainingPerformance(Speedup

Vs

K80)100x8xV100cuDNN780xSpeedup

vs

K8085%

Scale-Out

EfficiencyScales

to

64

GPUs

with

MicrosoftCognitive

Toolkit0510158XV1008XP100Multi-NodeTraining

with

NCCL2.0(ResNet-50)ResNet50Training

for90

Epochs

with

1.28M

images

dataset

|

Using

Caffe2

|

V100performance

measured

onpre-productionhardware.64X

V100

1

Hour7.4

Hours18

Hours3X

Reduction

in

Time

to

TrainOver

P100010201XV1001XP1002XCPULSTMTraining

(Neural

Machine

Translation)NeuralMachineTranslationTraining

for

13Epochs

|German

->English,

WMT15

subset

|

CPU

=

2x

Xeon

E5

2699

V4

|

V100

performance

measured

onpre-production

hardware.15

Days18

Hours6Hours18Deep

Learning-Inferencing:TESLAV100

DELIVERS

NEEDED

RESPONSIVENESS

WITH

UP

TO

99X

MORE

THROUGHPUT01,0002,0003,0004,0005,000CPU

ServerTeslaV100ResNet-504,647

i/s@7mslatency47

i/s@21msLatency06001,2001,800CPU

ServerTeslaV100VGG-161,658i/s@7msLatency23

i/s@43msLatency05001,0001,5002,0002,5003,0003,500CPU

ServerTeslaV100GoogleNet3,270

i/s@7msLatency136i/s@7msLatencyThroughput

(Images/Sec)Throughput

(Images/Sec)Throughput

(Images/Sec)GPUServers:

Dual

Xeon

E5-2690

v4@2.6GHz

with

16GB

PCIe

GPUs

configs

as

shownUbuntu

14.04.5,

CUDA

9.0.103,

cuDNN

3;

NCCL

2.0.4,

TensorRT

pre-release,

data

set:

ImageNet,GPU

Optimalbatchsize

used

toachieve

7mslatency;

CPU

batchsize

reduced

to

1if

latency

exceeds

7msCPU:

Xeon

E5-2690

V4NVIDIA

METROPOLIS

—EDGETO

CLOUDAI

CITYPLATFORMCameraCLOUDTraining

and

InferenceEDGE

ANDON-PREMISESInferenceDGX

ApplianceVideo

recorderServerJETSONTESLA/QUADROJETPACK,

TENSOR

RT,

DEEPSTREAM200102030405060HPCG

Performance

EquivalencySingle

GPU

Server

vs

Multiple

CPU-Only

ServersCPU

Server:

Dual

Xeon

E5-2690

v4@2.6GHz,

GPU

Servers:

same

CPU

server

w/V100s

PCIeCUDA

Version:

CUDA

9.0.103;

Dataset:

256x256x256

local

sizeTo

arriveatCPU

node

equivalence,

we

use

measured

benchmark

withup

to

8

CPU

nodes.

Then

we

use

linear

scaling

toscalebeyond

8nodes.HPCGBenchmarkExercises

computational

and

data

accesspatterns

that

closely

match

a

broad

set

ofimportant

HPC

applicationsVERSION3ACCELERATED

FEATURESAllSCALABILITYMulti-GPU

and

Multi-NodeMORE

INFORMATIONhttp://www.hpcg-/index.html19

CPUServers37

CPUServers67

CPUServers#

of

CPU

Only

Servers701server8x

V100GPU’s19x36x67xSpeed

up

vsCPU

server1server2x

V100GPU’s1server4x

V100GPU’sREINVENTING

OUR

COMMUNITYREINVENTING

OUR

COMMUNITY23AUTOMOTIVE

DEEP

LEARNINGDeepLearning

for

Damage

Estimation

based

on

photo

of

the

damageCustom

development

for

one

of

top

5

InsuranceCompaniesData

Processed

to

Date*

Total

Data

Available90,000

Claims~380,000

car

imagesScope

of

Work1000

Claims4500

images*Time

lag

in

data

migration,data

clean-up

and

tagging

imagesVisualization

of

the

activations

of

theconvolutions

over

a

car

damage

imageVisualization

of

the

layersof

a

neuralnetwork

and

the

back

propagation

processNeural

Network

Demohttps://www.galacticar.ai/

->

Galaxy.ai

:

Artificial

Intelligence

Driven

Sedan

Damage

EstimatorSoftware

as

a

ServiceAnnual

fee

+

fixed

feeevery

time

API

is

pingedIntegration

with

Insurance

Mobile

app.Use

cases

verified

by

the

industry

and

insurance

clients:Whether

clientshould

file

a

claim

or

not

-

Claims

triagingClaims

estimation

from

damaged

sedan

vehicle

imagesBusiness

Model(1)

DEEP

LEARNING

IN

FINANCE

-TRADING/gtc/2016/presentation/s6589-masahiko-todoriki-performance-improvement-algorithmic-trading.pdf/

20

Oct

2016Generic

Bigdata

OperationsData

AnalyticsData

VisualizationScrambling&ProtectingDataGovernancePublishedDataMarts/LakeHarmonization&

OntologyMappingDataHarmonizationDataQuality

&IntegrityDataCleansingDataStagingDataAcquisitionData

PreparationData

DisseminationData

Warehousing/Storage

(including

Network

Data)Virtualized

Platform

&

Security

ManagementHarmonizationTerminologiesCheck

fordifferentRepresentationand

usageOntologyCorrection&

AssuranceCheck

forCorrectionExceptionFields/DataDataAnonymity

&DataProtectionStructuredDataSourcesStructuredData

AccessDataModelingSentimentAnalyticsData

Reporting&

VisualizationDataInterpretationSocial

NetworkUnderstandingReal-timeDataSourcesReal-time

DataIngestionDataAnalyticsNetworkAnalyticsUnstructuredData

SourcesUnstructuredDataCollectionData……….DataStatisticsMobileSharingTabletSharingPCSharingPush

&

Pull

Data

PlatformData

Models&Schema29GPU

DATABASES

ARE

EVEN

FASTER1.1Billion

Taxi

Ride

Benchmarks2130156080991250150269225037269629705000450040003500300025002000150010005000Query

1Query

2Query

3Query

4Time

in

MillisecondsMapDDGX-1 MapD

4

xP100Source:

MapD

Benchmarks

on

DGX

from

internal

NVIDIA

testing

following

guidelinesofMark

Litwintschik’s

blogs:

Redshift,6-node

ds2.8xlarge

cluster

&

Spark

2.1,

11

x

m3.xlarge

cluster

w/

HDFSRedshift

6-node Spark

11-node@marklit8210190

8134

19624

85942NVIDIA

CONFIDENTIAL.

DO

NOTDISTRIBUTE.

30Silicon

Valley-based

Blue

RiverTechnology

has

developeda

deep

learning

solutioncalledLettuceBot

that

rolls

through

afield

photographing

5,000young

plants

a

minute,

usingalgorithms

and

machine

visionto

identify

each

sprout

aslettuce

or

a

weed.

Thecompany

trained

their

neuralnetwork

with

GPUs

and

theCaffe

deep

learningframework.Accurate

within

a

quarter

inch,the

LettuceBot

automaticallypinpoints

weeds,underdeveloped

sprouts,

andoverplanted

areas

and

thenapplies

tiny

doses

of

herbicideto

maximize

crop

production.Automated

Crop

ManagementSource

:

/artificial-intelligence-helping-to-ensure-humanitys-future-food-supply/NVIDIA

CONFIDENTIAL.

DO

NOTDISTRIBUTE.

31Researchers

from

the

CostaRica

Institute

of

Technologyand

French

AgriculturalResearch

Centre

forInternational

Developmentdeveloped

a

deep

learningalgorithm

toautomaticallyidentify

plant

specimens

thathave

been

pressed,

dried

andmounted

on

herbarium

sheets.According

to

the

researchers,this

is

the

first

attempt

to

usedeep

learning

to

tacklethedifficult

taxonomic

task

ofidentifying

species

in

natural-history

collections.Artificial

Intelligence

Helps

Identify

Plant

SpeciesSource

:

/artificial-intelligence-helps-identify-plant-species-for-science/32Trainwhole

slide

imagesamplesampletraining

datanormaltumorTestwhole

slide

imageoverlapping

imagepatchestumor

prob.

map1.00.00.5Convolutional

NeuralNetworkP(tumor)How

does

it

work?SAFE

AND

SMART

CITIES

IS

AN

AI

PROBLEM0M200M400M600M800M1,000M201620201

billion

installed

security

cameras

WW(2020)30

billion

frames

per

dayChallenging

real

world

conditionsTraditional

video

analytics

nottrustworthyAccuracyImage

Classification9…HumanDeep

Learning74%Hand-coded

CV2010

2011

2012

2013

2014

2015

2016AIachieves

super

human

resultsAI

driven

intelligent

video

analytics34353639AI

FORVISUAL

SEARCHIN

MARKET

PLACE40TESLA

PLATFORMLeading

Data

CenterPlatform

for

HPC

and

AITESLA

GPU

&

SYSTEMSNVIDIA

SDKINDUSTRY

TOOLSAPPLICATIONS&SERVICESECOSYSTEM

TOOLS

&

LIBRARIES+400

MoreApplicationsHPCcuBLAScuDNNTensorRTDeepStreamSDKFRAMEWORKSMODELSCognitive

ServicesAI

TRAINING

&

INFERENCEMachine

Learning

ServicesResNetGoogleNetAlexNetDeepSpeechInceptionBigLSTMDEEP

LEARNING

SDKNCCLC/C++COMPUTEWORKSTESLA

GPU

NVLINK

SYSTEM

OEM

CLO