the evolution of ai in the network edge · 1 the evolution of ai in the network edge remi...
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![Page 1: The Evolution of AI in the Network Edge · 1 The Evolution of AI in the Network Edge Remi El-Ouazzane, COO/VP, AI Products Group, Intel @relouazzane GSA Executive Forum, Sept 18,](https://reader034.vdocuments.net/reader034/viewer/2022052014/602b05baedadb726c6307fff/html5/thumbnails/1.jpg)
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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
![Page 3: The Evolution of AI in the Network Edge · 1 The Evolution of AI in the Network Edge Remi El-Ouazzane, COO/VP, AI Products Group, Intel @relouazzane GSA Executive Forum, Sept 18,](https://reader034.vdocuments.net/reader034/viewer/2022052014/602b05baedadb726c6307fff/html5/thumbnails/3.jpg)
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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
![Page 4: The Evolution of AI in the Network Edge · 1 The Evolution of AI in the Network Edge Remi El-Ouazzane, COO/VP, AI Products Group, Intel @relouazzane GSA Executive Forum, Sept 18,](https://reader034.vdocuments.net/reader034/viewer/2022052014/602b05baedadb726c6307fff/html5/thumbnails/4.jpg)
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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
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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