Ternary Computing for a Human-Cyber-Physical Universe

Zhiwei XuInstitute of Computing Technology

www.ict.ac.cnzxu@ict.ac.cn

INSTITUTE OF COMPUTING

TECHNOLOGY

NSF Workshop2011.9.19-20

The FIT Initiative of Chinese Academy of Sciences

• One of the seven Frontier Research Projects• Bio, Space, Earth, Climate, Fission, Coal, IT

– Future Information Technology utilizing human-cyber-physical resources (ternary computing)

• A 10-year basic research project– Targeting applications and markets of 2020-2030– Addressing China’s needs in 2020-2050

• Main components: – functional sensing– customizable internet– cloud-sea computing– science of information ecosystems

China’s Needs (2020-2050)• Change into sustainable development with the four

simultaneous, historical constrains of – globalization, industrialization, urbanization, informatization,

• Need computing for the masses, ternary computingZ. Xu and G. Li, Computing for the masses, Communications of ACM, October

2011,vol. 54, no. 10, pp.133-141.

GDP% 1st 2nd 3rd

1993 19.5 46.6 33.9

2010 10.2 46.8 43.0

2050 5.8 42.5 51.7

The industry sector will dominate the national economy for decadesThe industry sector will dominate the national economy for decades

The urbanization rate will increase from 49.7% in 2010 to 80.0% in 2050

The urbanization rate will increase from 49.7% in 2010 to 80.0% in 2050

The IT market will increase from $0.15T, 400M users in 2010 to $2T, 1.2B usersin 2050

The IT market will increase from $0.15T, 400M users in 2010 to $2T, 1.2B usersin 2050

Example: Industrialization• >200 million migrate workers in China• 2010 China furniture industry: $140B• Manufacturing equipment: 50% cost of is IT

– Needs smart equipment: current 3% 40%

• Expertise-enabled Computer Numeric Control (E2CNC)– Expertise: domain knowledge, professional experience, know-how

Polish MachinePolish Machine

Smart curve saw:

25 meters/minute

0.1 0.05 mm

saved 6 KW power

Smart curve saw:

25 meters/minute

0.1 0.05 mm

saved 6 KW power

Example: Urbanization• >200 million households in urban China, >4 million added every year

– Need IT to help popularize a sustainable life style• Electricity consumption by Beijing households in 2008:

– 11.63 billion KWH, 16.7% of the total electricity consumption– Per-household KWH: 15000 (high), 1200 (low), 600 (green), 1320 (policy)

• China’s CO2 emission (tons) in 2008:– 5.96 billion (total), 4.5 (per capita), 2.7 (household), 0.96 (green household)

• Grid search and behavior optimization

Timely acquire massive and accurate field data from 100s millions households, for each appliance (lamp, refrigerator, etc.) in every household. with one sensor per home

Electricity Computing: let the physical world do the job

Example: Informatization

• 485 million netizens in China now (CNNIC, 2011.7)

• An Internet C2C service (Taobao, cf. eBay)– 2010: >200M users (80M UV), >2.5M vendors (>50% women), 2B items– $59B GMV (2.5% of $2.35T), 10M items delivered/day, ~$16/item– 2014 (estimation): $300B GMV, 32B items (merchandise & services)

• Increase delivered value (or value/item) at low cost– Human-aided big data mining & analytics (20PB 200PB)– Big data augmented C2B– Better platforms: 1 week 3 months data; response 2.6 1.1s

Historic Data and Projections IT Market($ Trillion)

IT MarketCAGR

IT Users(Million)

IT Spending per Capita

2000 (Actual Data) 0.026 25.0% 22.5 $21

2008 (Actual Data) 0.11 12.7% 270 $85

2050 (Poverty Line Growth) 0.25 2.0% 1,200 $190

2050 (Value-Augmenting Growth) 2.0 7.1% 1,200 $1,321

Alexa Top Sites

1. Google2. Facebook3. YouTube4. Yahoo!5. Baidu6. Wikipedia7. Blogger8. Windows Live9. Twitter10. QQ

16. Taobao17. Sina21. eBay

Alexa Top Sites

1. Google2. Facebook3. YouTube4. Yahoo!5. Baidu6. Wikipedia7. Blogger8. Windows Live9. Twitter10. QQ

16. Taobao17. Sina21. eBay

1960-2000 vs. 2010-2050

Algorithmic Science

NewInformation

Science

• Man-machine symbiosis Ternary Universe (The Net)

• The scope and objects of computer science are changing– Cyber Computing Ternary Computing

– Turing algorithmic science algorithm Net science

– Moore’s law Network Effects

Example of Utilizing Ternary Resources

Human Society Cyber world

Physical World

Bill

Human meter reading15 bytes/month

electrical appliances physical behavior of

using electricity15 GB/month

200 million families’ Electricity

consumption behavior

（ habits, economic incentives, social

relationship）

Automatically sense human society and physical world

Search optimal behavior of electricity consumption

Promote best practices of energy consumption

Energy Saving: In 2009, an average household in China consumed 1044 KWH

But a green households in Beijing only consumed 600 KWH

By 2030, household electricity consumption could be reduced by

30% through sensing and promoting green practicesUpgrading Household Appliances: New energy-saving appliances as data-intensive as

Rolls-Royce aircraft engines

• Professional challenges– The DARPA Red Balloon Challenge

requires integrating Human-Cyber resources

• Major research initiatives– EU FET Flagships proposals (e.g.,

FuturICT) involve ternary integration

• Specific research results– ReCAPTCHA utilizes Human-Cyber

resources – SignalGuru utilizes Human-Cyber-

Physical resources

Ternary Computing Research Is Starting

Connectivity and Integration of People, Machines, Things

Connectivity

Seamless

Connected

Disjoint

None Digital Information Functional (behavior, cognition)

Integration

Level

Red Balloon

SignalGuru

ReCAPTCHA

Grid Search

FIT Scope

Traditional

Turing

Computing

Capability Upgrade through FIT Innovations

Information Technology

Material Device  Equipment System

Acquisition

Transmission

Processing

Application

Informationization Capability

Informationization

Physical parameter sensing

Best-effort packet switched networks

Cloud computing handling PB scale (1015 bytes) of data

Internets of Things, Media, Services (100s million users, billion hosts)

Function sensing of physical world and human society

Evolvable internet with end-to-end quality assurance

Sea-cloud computing handling ZB scale (1021 bytes) of data

Pervasive intelligent services with Human-Cyber-Physical integration (billions of users, trillions of devices)

FITInnovations

Addressing constraints of power and security

Addressing constraints of power and security

Speed, power, software complexity trendsthe Three 100-million issues

2020

Exaflops (1018)Datacenter for100’s M (108) users

100 M (108) LOC100 M (108) W

Needs:

Maintain growth in performance, but control power & system software complexity

World Top1 computer speed (Flops)

ICT computer speed (Flops)

ICT computer system software (LOC)

ICT computer power (W)

Functional Sensing• Compressive Sensing• Eliminating redundant data in the data acquisition phase (A-to-D A-to-I)• Functional Sensing

– From sensing physical parameters, sensing information, to sensing behavior• Function: formalized cognition or behavior

– Learn from biological perception networks• Goal: further reduce sensed data amount by 1~2 orders of magnitude

FunctionalSensing

Cognitioninformation

Behavior

Information reduction

Customizable Internet• Main features

– Extend endpoints to physical devices and people– Programmability, isolation (slices), high performance– Behavior cognition and cross-layer optimization

• 2010-2015: – Enable research (host-oriented, content-centric, etc.)– basic research and testbed experiments

PEARL routers in one physical networkPEARL routers in one physical networkEach PEARL routers provides 4 Gbps ports and customizable data/ctrl planes, Each PEARL routers provides 4 Gbps ports and customizable data/ctrl planes,

and support 128 virtual routersand support 128 virtual routers

IPv4IPv4IPv6IPv6

Non-IPNon-IP

G. Xie et al, “PEARL: A Programmable Virtual Router Platform”, IEEE Communication, July 2011

Cloud-Sea Computing

• Sea Computing– A new computing model– hierarchically self-organizing

resources of front-end nodes– to generate local intelligence– to perform 90% sensing data

processing– There will be many sea terminals

• Sea-Cloud Computing– Cooperatively divide and schedule

computing tasks at the sea side and the cloud side

– Big data processing and massive serving are carried out in the cloud

• Optimize performance/energy ratio

Ecosystems ScienceUser Experience

Service

Application

Middleware

System Software

Machines

Components

1955-1980Vertical

1980-2005Horizontal

2005-2030End-to-end ecosystems

IBM DEC ……

EDS, Andersen, …

MS Office, SAP, …

Oracle, BEA, …

Windows, Unix, …

HP, Cisco, Dell, …

Intel, Seagate, …

IBM, Accenture, …

MS Office, SAP, …

Oracle, BEA, LAMP, …

Android, Windows, Linux…

HP, Cisco, Lenovo, …

Intel, ARM, Seagate, …

Apple

Facebook

Google

Tencent

Smart grid, public safety, intelligent traffic systems, etc.Value in productivity, sustainability, welfare and well-being

Functional sensing, customizable internet, sea-cloud computing systemsFIT architecture, ternary computing models, security and privacyPhenomena, metrics, laws, abstractions, mechanisms

Time/space complexityEnergy complexity; effort complexity, sensor complexity

Physical Information Media Information Social Information

Applications (IIS)

Systems (CNS)

Foundation (CCF)

Open Problems• What are the new workloads?

– “real” workloads open to academic community

• What should be the new metrics?– Beyond Linpack and flop/s– Can we calculate energy complexity for each application?

• What is a good stack?– What new properties? How to evaluate a stack?

• How to deal with the “Classis Insecta Paradox”?– Current IT: mammals (5000 species)– Future IT: insects (5 million species)

New SystemsArchitectures

• Need computing systems enabling– personalization, specialty,

and large volume• Learn from IBM 360 in 1964

– Computer family and computer architecture• To deal with the “Classis Insecta Paradox”,

we propose– Computer tribe– Elastic processor

Q. Guo, T. Chen, Y. Chen, Z. Zhou, W Hu, Z. Xu, Effective and Efficient Microprocessor Design Space Exploration Using Unlabeled Design Configurations, IJCAI 2011.

Q. Guo, T. Chen, Y. Chen, Z. Zhou, W Hu, Z. Xu, Effective and Efficient Microprocessor Design Space Exploration Using Unlabeled Design Configurations, IJCAI 2011.

Current chip design solutions

• Has user definable microarchitecture that can be changed dynamically to adapt to applications’ requirements

• 2 orders of magnitude improvement in power efficiency

ASICHardwired solution

GPPSoftware solution

Elastic Processor

Flexibility

MOPS/mW

FPGAField and gate-

level reconfiguratio

n

Elastic Processor

Many US scientists are researching similar issues

NSF Expedition project:Customizable Domain-Specific Computingwww.cdsc.ucla.edu

GreenDroid at UCSD Utilization wall Conservation coresIEEE Micro, 3/4 2011

Many US scientists are researching similar issues

NSF Expedition project:Customizable Domain-Specific Computingwww.cdsc.ucla.edu

GreenDroid at UCSD Utilization wall Conservation coresIEEE Micro, 3/4 2011

Thank Thank you!you!

zxu@ict.ac.cn