Jerry Chen, Head of Industry Development for AI in Manufacturing

November 2019

THE UPSIDE OF AI IN THE INDUSTRIAL ECONOMY

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NVIDIA

GRAPHICS AI

GAMING DESIGN CGI AI INFRASTRUCTURE AV & ROBOTICS AI FOR INDUSTRIES

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EVOLUTION OF COMPUTING

1995 2005 2015

PC InternetWindows, Yahoo!1 billion PC users

Mobile-CloudiPhone, Amazon AWS2.5 billion mobile users

AI & IOTDeep Learning, GPU100s of billions of devices

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A WORLD OF DATA

40M Miles

of road

570M Farms 2M Factories

10T Miles drivenper year

13M Stores 5M Call center agents

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ACCELERATED DATA SCIENCE

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COMPUTERS WRITING SOFTWARE

DATA

PRE

PO

ST

PROGRAMDEEP NEURAL NETWORK

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1980 1990 2000 2010 2020

GPU-Computing perf

1.5X per year

1000X

by

2025

RISE OF GPU COMPUTING

Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham,

K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp

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103

104

105

106

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Single-threaded perf

1.5X per year

1.1X per year

APPLICATIONS

SYSTEMS

ALGORITHMS

CUDA

ARCHITECTURE

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NVIDIA AI BODY OF WORK

Reinforcement Learning Meta Sim 3D Segmentation

3D Pose Estimation Leonardo — Kitchen Robot Semantic Segmentation BB8 — Self-driving Car

GauGAN

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GPU-ACCELERATED DATA SCIENCEUse Cases in Every Industry

Ad Personalization

Click Through Rate Optimization

Churn Reduction

CONSUMER INTERNET

Claim Fraud

Customer Service Chatbots/Routing

Risk Evaluation

FINANCIAL SERVICES

Manufacturing Inspection

Failure Prediction

Demand Forecasting

MANUFACTURING

Detect Network/Security Anomalies

Forecasting Network Performance

Network Resource Optimization (SON)

TELECOM

Supply Chain & Inventory Management

Price Management / Markdown Optimization

Promotion Prioritization And Ad Targeting

RETAIL

Personalization & Intelligent Customer Interactions

Connected Vehicle Predictive Maintenance

Forecasting, Demand, & Capacity Planning

AUTOMOTIVE

Sensor Data Tag Mapping

Anomaly Detection

Robust Fault Prediction

OIL & GAS

Improve Clinical Care

Drive Operational Efficiency

Speed Up Drug Discovery

HEALTHCARE

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AI FOR MANUFACTURING

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$13T 20% 4% 31%Global

Manufacturing Value Added

Increase Production Capacity

Lower Material Consumption Rates

Increase Overall Equipment

Effectiveness

AI MANUFACTURING OPPORTUNITY

The World Bank: DataBankTech’s Next Big Wave: Manufacturing, Morgan Stanley

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ONE PLATFORM ACROSS INDUSTRIAL USE CASES

DESIGNHPC

Modeling & Simulation

Design for Manufacturability

SUPPLY CHAINForecasting & Inventory Management

Supply Chain Optimization

Robotics & Automation

MANUFACTURERobotics & Automation

Inspection

Predictive Maintenance

Process Control

SERVICEPredictive Maintenance

Field Inspection

Logistics Optimization

Parts Inventory Management

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MANUFACTURING & INDUSTRIAL AI USE CASES

Quality Inspection

& Test

Monitoring and

DiagnosticsField Inspection

Supply/Demand

Forecasting

Operational Process

Automation

MANUFACTURING INSPECTION

PREDICTIVE MAINTENANCE

VIDEO ANALYTICS SUPPLY CHAIN & LOGISTICS

SPEECH & LANGUAGE

Yield and

ThroughputUptime

Health & Environmental

Safety

Scheduling & Route

Optimization

Work Scope

Optimization

“What’s the calibration

procedure for replacing

this component?”

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TITLE ONLY SLIDE

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INSPECTION BUSINESS VALUE

Assembly VerificationEach line averages 1 escape per day. Up to

$50,000 saved per minute by avoiding

production stoppage

Full Car InspectionFull vehicle inspection in seconds vs

minutes

Sheet Metal StampingDouble the inspection throughout

Half the inspection latency

Defects as small as 50 microns

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AI FOR PROFITABLE OPERATIONS

AI can provide greater control over

complex supply chains can

contribute to an estimated $134B in

cost savings. AI use cases include

forecasting, demand modeling,

capacity planning and supply chain

optimization.

New smart robots can use AI for

visual inspection working alongside

humans on factory floors and

assembly lines.

With hundreds of sensors in

connected vehicles, AI can be used

to detect problems before they

affect vehicle operation.

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Example: Reflection ArtifactsGap Detection & Measurement

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EXAMPLES: INSPECTION IN AUTOMOTIVE

Sheet Metal Stamping CosmeticParts, Paint

Missing Components Optical Character Recognition

Assembly Verification Leather, Mesh, Textile KittingFull Vehicle Final

Inspection

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END-TO-END INDUSTRIAL INSPECTION

NGC

NVIDIA EGX Server

NVIDIA Edge Stack

NVIDIA Metropolis Application Framework

ANY CLOUD

Review

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PAPER MILL DOWNTIME PREDICTIONA major European paper producer experiences

paper web breaks several times a week, leading

to production downtime resulting in revenue

loss of $2 million per year per machine.

Using deep neural networks developed on

NVIDIA GPUs, Conundrum’s predictive

maintenance solution improves the true defect

prediction rate by 1.6x, reducing downtime by

24% to realize an annual $10M productivity

improvement per paper mill plant.

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GPUS FOR SEMICONDUCTOR DESIGN & MANUFACTURING

DESIGN BUILD INSPECT & TEST

Intelligent Verification

Place & Route Optimization

Hotspot Prediction

Defect Detection & Classification

Bump Inspection

Dynamic Test Coverage

Calibrated Yield Prediction

Computational Lithography

Fault Detection & Classification

Predictive Maintenance

Intelligent Process Control

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WAFER QUALITY INSPECTION

Defective wafers have costly consequences to downstream

production. SPIL uses AOI to detect defects, but its AOI

platform produces up to 70% false positives ― which

then require a 2nd inspection.

To improve effectiveness SPIL implemented an AI-based

2nd level inspection system powered by NVIDIA DGX-2

for training and NVIDIA T4 for inferencing. NVIDIA

TensorRT Inference Server is used to manage

multiple models.

SPIL now detects 100% of defects with <10%

false positives ―a 7X improvement— and

easily scales production line models

from 10 to 100.

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FROM AUTONOMOUS TO INTELLIGENT FACTORIESSeagate processes 10TB of multi-modal data daily to

maintain the highest quality standards. Long latencies

and limitations of rules-based analytics prevented

Seagate from increasing quality inspection throughput and

scaling globally.

Seagate is deploying an AI inspection system with

the NVIDIA EGX edge computing platform to

accelerate inference on the factory floor.

The new system will detect anomalies

in real-time and minimize

costly false positives.

Impact:

• Up to 10% increase in throughput

• 20% reduction in clean room investment

• Up to 300% ROI from improved quality and

operational efficiencies

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INTELLIGENT MATERIALS DESIGNThe discovery of new materials is crucial to many industries.

But developing new materials either experimentally or

computationally can take many years.

Researchers at Samsung are using GPU-powered deep

learning to perform inverse design — using AI to

propose new materials with the necessary properties

for the development of blue phosphorescent OLED.

With this approach Samsung can design new

materials 52x faster than traditional

methods — delivering

results in only

one month.

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VLSI PLACEMENT

Central role in the VLSI flow, large impact on design closure

[Courtesy RePlAce]

Synthesis

Placement

Clock Tree

Routing

Xavier SoC: 9B TransistorsDivide into 100s of unique partitions 1~2M logic gates (cells) per partition

Iteration time through backend flow3~6 days for one partition,Many iterations per tapeout

Placement:Find the optimal location for all cells in a partition

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MANAGE DEPLOY TRAIN PUBLISH

NVIDIA CLOUD TO EDGE COMPUTING

NVIDIA EGX NVIDIA DGX

EDGE TO CLOUD

NVIDIA AGX

Auto

Security

Retail

Construction

Manufacturing NVIDIA HGX

NGC HYBRID & MULTI-CLOUD

ANY CLOUD

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NVIDIA SATURN-V DATACENTER

COMPUTE & AI BY NVIDIA

NETWORK, STORAGE, SECURITY BY MELLANOX

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NVIDIA EGX EDGE COMPUTING

.5 TOPS

COMPUTE & AI BY NVIDIA

NETWORK, STORAGE, SECURITY BY MELLANOX

10,000 TOPS520 TOPS

A new class of distributed AI computing systems

designed to gather and analyze continuous streams

of data at the edge of the network.

AI computation is performed largely or completely

on the EGX systems close to the data or user.

NVIDIA EGX is for applications that require:

Low-latency interactions

Reduced bandwidth to the cloud

Data privacy or sovereignty

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NVIDIA DLI (DEEP LEARNING INSTITUTE) – GET YOUR WORKFORCE AI READY

Robotics Industrial HealthcareVideo Analytics

Digital Content

INDIVIDUALOnline

Learning

TEAMSInstructor Led

Workshops

UNIVERSITIESTeaching

Kits

DEEP LEARNING FUNDAMENTALS

ACCELERATED COMPUTING FUNDAMENTALS

DEEP LEARNING BY INDUSTRY