significant value of endpoint computing

Significant Value of Endpoint Computing

Tomomitsu Maoka

SVP, Renesas Electronics Corporation

Chairman, Renesas Electronics China

DECLINING BIRTH RATE AND AGING POPULATIONDECREASE IN THE NUMBER OF EMPLOYEES IN MANUFACTURING

US

Germany

China

France

Japan

50

100

1980 2000 2020 2040

Production age population (15-64 years old)

←set 2014 to 100

From Government data book 2015 From Ministry of Internal Affairs and Communications

Economic effect

To improve productivity

Reduce waste / cost

Continuous growth

independent of labor force

Employee population in Japan

Ratio [%]

Number of

employees [M]

Number of

employees [M]

Ratio

[%]

2

ACTIVITIES TO IMPROVE PRODUCTIVITY

Industry 4.0 The 4th Industrial Revolution

Digitizing information Digitizing factory

Big data analysis Cloud, IoT technology

Micro service Agile development

Distributed data processing Block chain

Optimize on-premises and cloudsAI, Deep learning

3

ACTIVITIES TO IMPROVE PRODUCTIVITY

Industry 4.0 The 4th Industrial Revolution

Digitizing information Digitizing factory

Big data analysis Cloud, IoT technology

Micro service Agile development

Distributed data processing Block chain

Optimize on-premises and cloudsAI, Deep learning

Keyword is

Information

Digital transformation

(Digitizing)

Cyber physical system

(Modeling)

Mass customization

(Connecting, optimizing)

4

10,000,000

100,000,000

1,000,000,000

10,000,000,000

100,000,000,000

1,000,000,000,000

10,000,000,000,000

100,000,000,000,000

2007 2012 2017 2022 2027 2032 2037

Sensors/year

From Trillion Sensors Universe, TSensors Summit

2023

over 1T

USE OF INFORMATIONTHE TIME OF “TRILLION SENSORS” WILL COME

960Tbit/sec data

will be generated

Estimated by Renesas

5

INFORMATION DATA PER EMPLOYEE IS 5T BYTES/YEAR

◆Shipment of sensors

2013: 10 billion pieces

2023: 1 trillion pieces

◆Employees in industry

2013: 724 million people

2023: 760 million people*1

Sensors : About 100 times (2013→23)*2

Information data : 5T Bytes/employee/year

Capital investment is necessary

to utilize huge amount of information*1 ILO「GLOBAL EMPLOYMENT TRENDS 2014」、

and government data book in 2015, and estimated by Renesas

*2 Estimated by Renesas

6

NEED TO COLLABORATE AMONG DIFFERENT INDUSTRIES

How much info can be used becomes competitive

Endpoint computing devices Embedded software

Cloud Service / Application

New technologies

New skills

7

SIGNIFICANT VALUE OF ENDPOINT COMPUTINGAS ORIGINAL SOURCE OF INFORMATION DATA

Endpoint computing devicesEmbedded software

Cloud Service / Application

New technologies

New skills

Toward predictive maintenance

to use real-time information

8

Cloud` `

Factory

PROBLEM OF ENDPOINT

Endpoint computing devices

Increase in sensors and increase processing of endpoints

Networktightness

- Huge data generated

- Real-time performance

- Network bandwidth

Importance of endpoint roles

Intelligent processing required

9

Cloud

Factory

SOLUTION TO MAKE ENDPOINTS INTELLIGENT

Data processing at endpoints

Autonomy by AI

“e-AI solution”embedded Artificial Intelligence

Uploadjudgment only

Distribution between cloud

and endpoint

Continuous real-time processing

by e-AI solution

10

e-AI CREATES NEW BUSINESS OPPORTUNITY

Statistical AI (Sampling data)

Realtime AI (Continuous data)

Learning

＆

Inference

Inference mainlyPre/One-time Learning

CloudAI

Endpointe-AI

EdgeAI

New Biz opportunity

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Not “Zero-sum” But “Plus-sum”Co-creation of Two AIs Enables New Big Biz Opportunities

Cloud AI Endpoint e-AI

Sensor Actuator Sensor Actuator

Computing

(AI)

Distributed AutonomousComputing

(AI)Co-creation

Towards Plus SumDiversified Optimization

Fusion among sensor, actuator

and Computing

Centralized

12

e-AI CAPABILITY ENHANCED BY DRP

DRP: Dynamically Reconfigurable Processor

e-AI Capability

2018 2019 2020 2021 20222017

Endpoint Inference

e-AI on MCU / MPUClass-1

x10Realtime Image Processing

by DRP

Realtime Cognition

by DRP-AI

Endpoint Incremental Learning

by DRP-AI 2

Class-2

Class-3

Class-4

x10

x10

Solution released July 2017

Product release October 2018

Paper reported VLSI symp. 2018

1.5 years ahead of competition

13

MES FDC

e-AI

result

e-AI

execution

Settings

PLC

Sensors

EXPERIMENT CONFIGURATIONWITH NEW FDC SYSTEM

Added judgment

by e-AI

AI ※ In this experiment, we used AI developed by M-IX of Xcompass Ltd.

14

SUCCESS BUSINESS CASESSMART FACTORY MOVES FROM PREVENTIVE MAINTENANCE TO PREDICTIVE MAINTENANCE

e-AI Deployed at Renesas Semiconductor Factory

150 3000!x20

e-AI Solution “AI Unit” at Renesas Factory

Add-On e-AI Systems

“AI Unit”

Renesas Naka Wafer Fabrication Factory

Production lead time reduced by one-third with implementation of

predictive maintenance

Annual $10 million USD value for entire system

故障低減によるリードタイムの短縮

生産工程 故障による作業待ち

装置作業待ち

ウェハ生産リードタイムWafer Production Lead Time

Lead Time Reduction by Prevention of Failure

Production

Process

Scheduled

Down TimeUnscheduled

Down Time

15

e-AI CAPABILITY ENHANCED BY DRP

DRP: Dynamically Reconfigurable Processor

e-AI Capability

2018 2019 2020 2021 20222017

Endpoint Inference

e-AI on MCU / MPUClass-1

x10Realtime Image Processing

by DRP

Realtime Cognition

by DRP-AI

Endpoint Incremental Learning

by DRP-AI 2

Class-2

Class-3

Class-4

x10

x10

Solution released July 2017

Product release October 2018

Paper reported VLSI symp. 2018

1.5 years ahead of competition

16

DRP, ACCELERATOR OF DEEP NEURAL NETWORK WITH RAPID CHANGE

INPUT OUTPUT

DNN contains

・Convolution Layer

・Pooling Layer

・Full Connected Layer

・Activation Function

Multiple Parallel Processing is Required

Renesas Utilizes DRP as AI Accelerator

17

Latest Demonstration of RZ/A2MProvide Real-time and Low Power Performance

DRP case（only 40MHz）

Fixed 10.4msecCPU case（500MHz）

About 140msec10 Times

18

USE CASE CLASS-1: E-AI ANOMALY DETECTION FOR HUNDREDS MILLION MOTORS

Benefits:

▪ Improve service quality

▪ Avoid downtimes

▪ Reduce maintenance cost

Renesas is shipping 200M+ motor control MCU per year.

New MCU series will enable e-AI Anomaly Detection.

Everywhere Every Time

Smart Living Smart InfrastructureSmart Factory

19

USE CASE CLASS-2 / 3: MULTIMODAL E-AI BIOMETRICS AUTHENTICATION

Smart cognitive system with new MPU products.

Biometrics Information

Authentication Data

MatchMatch Match Detect

Cashless

Airport

Office Entry Systems

Mobile Systems, Body-worn

Passport

ID Card

ID Card

Criminal Photo

20

COLLABORATION BEYOND INDUSTRY BOUNDARIESTO SOLVE SOCIAL ISSUES

Endpoint computing devices Embedded software

CloudService / Application

21

www.renesas.com

Thank you for your attention

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