institute of computing technology
DESCRIPTION
INSTITUTE OF COMPUTING TECHNOLOGY. NSF Workshop 2011.9.19-20. Ternary Computing for a Human-Cyber-Physical Universe Zhiwei Xu Institute of Computing Technology www.ict.ac.cn [email protected]. The FIT Initiative of Chinese Academy of Sciences. One of the seven Frontier Research Projects - PowerPoint PPT PresentationTRANSCRIPT
Ternary Computing for a Human-Cyber-Physical Universe
Zhiwei XuInstitute of Computing Technology
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
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!