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The Evolution of AIin the Network Edge

Remi El-Ouazzane, COO/VP, AI Products Group, Intel @relouazzaneGSA Executive Forum, Sept 18, 2018

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Computer Vision Then and Now

NOWthen

o Heuristic Algorithms

o Human Engineered

o Server Based

o AI / Deep Learning

o Real-time, High Accuracy

o Edge Based

1960

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Today we’ll see how semiconductor industry iscritical in the evolution of AI at the Edge

DEFINITION TRENDS

MARKETS AND EXAMPLES CHALLENGES AHEAD

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Devices • Things

EDGE-TO-CLOUD: DATA IS KEY DRIVER

LatencyBandwidth

Securityconnectivity

Core Network

CloudData Center

NETWORK HUBOR REGIONALDATA CENTER

EdgeCompute Node

By 2019, 45% of datawill be stored, analyzed, and acted on at the edge

Video • Healthcare • Manufacturing

Smart Buildings • Energy

Transportation • Retail

Public Sector • Logistics • Smart Cities Drivers for edge

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There is a growing opportunityfor ai at the edge

Data CenterTraining

Endpoint &Edge Inference

Data CenterInference

• 25%-35% CAGRin AI for silicon industry

• Growth largerat the Edge,compared toData center

2018 2023

TAM

Note: Endpoint and Edge excludes smartphones and client PCsSource: Intel, IDC, Gartner

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THE VIRTUOUS CYCLE DRIVING INNOVATION INAI AT THE EDGE

• Huge Compute

Requirements

• Low Latency

• Power Efficiency

• Local Data / Privacy

• Efficient Data Flow

• Better Algorithm Processing

• Efficient Memory Use

• Silicon Process

Technology

EDGE PROCESSINGREQUIREMENTS

CHIP TECHNOLOGYIMPROVEMENTS

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CITIES • STATE • FEDERAL FINANCE • BANKING INDUSTRIAL CASINO GAMING

TRANSPORTATION ROBOTICS DRONES

Public Safety & SurveilanceTraffic, Parking and LPR

Emergency Response

People Counting Customer(i.e. Gender, Wait Time)ATM Facial Recognition

Autonomous VehiclesPublic Safety (i.e. Bus/Rail)Traffic & People Counting

Emergency ResponseAsset Inspection (i.e. Windmill)

Security & SurveillanceResponsive Retail Advertising

Digital Home Assitant

Manufacturing AutomationIndustrial (i.e. Pipeline Welding)

Public Safety & SurveilanceFacial Recognition

Machine Vision Asset Inspection(i.e. Pipeline)

Augmented Reality

Edge AI capabilities enable AUTONOMOUS FEATURES IN MANY APPLICATIONS

HOME • RETAIL • SURVEILLANCE

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INDUSTRIAL EXAMPLE: SMART FACTORY

WAREHOUSE

CARRYING

PARKING SORTING

Smart Factory:

Generates an Estimate of 1PB data/day by 2020

Things & Edge Node Edge Network & Core Network Cloud

WAREHOUSE MANAGEMENT SYSTEM

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• Item Tracking

• Mobile Payment

• Store Analytics

• Inventory Management

• Customer Tracking

RETAIL EXAMPLE: SMART STORESRetail Store:

Huge Data Processing Requirements

Things & Edge Node Edge Network & Core Network Cloud

10Intel® Movidius™

DRONE EXAMPLE: LIFE SAVING WITH AI

https://www.youtube.com/watch?v=QJOMfDyhUyo

The Intel® Movidius™ Neural

Compute Stick was used with

Australia’s Little Ripper Lifesaver

UAV to monitor the New South

Wales coastline for sharks.

The NCS is a proof-of-concept

system, showing how modern AI

processing can take place ‘on

device’ (rather than in the cloud),

leading to faster danger detection

and shorter response times

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SILICON INDUSTRY HAS CHALLENGES TO OVERCOME TO MAKEAUTONOMY AT THE EDGE POSSIBLE

’18 ’19 ’20 ’21 ’22 ’23

DEEP NEURALNETWORKPERFORMANCE*InferencesPer Secondfor ResNet50,Batch Size = 1

AI PERFORMANCE*FOR ISO POWER AT 5~10 W

10

100

1000

10,000

SEMI INDUSTRY PROGRESS GOALS:REDUCED PRECISION NEURAL NETWORKSImproved performance moving from 16b tobinary, for example

NEURAL NETWORK COMPRESSION / SPARSITYReducing compute requirement for NN structure, and taking advantage of zeros in matrix computation

EFFICIENT MEMORYPower efficient memory access with improved bandwidth

ACCELERATORSBalancing fixed function performance with flexibility/programmability

COMPILER INNOVATIONTools making best use of new hardware

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*I360

The Future is in Our Hands