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INSTITUTE OF COMPUTING TECHNOLOGY

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 zxu@ict.ac.cn. The FIT Initiative of Chinese Academy of Sciences. One of the seven Frontier Research Projects

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INSTITUTE OF COMPUTING TECHNOLOGY

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  1. INSTITUTE OF COMPUTING TECHNOLOGY NSF Workshop 2011.9.19-20 Ternary Computing for a Human-Cyber-Physical UniverseZhiwei XuInstitute of Computing Technologywww.ict.ac.cnzxu@ict.ac.cn

  2. 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

  3. 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 computing Z. Xu and G. Li, Computing for the masses, Communications of ACM, October 2011,vol. 54, no. 10, pp.133-141. The IT market will increase from $0.15T, 400M users in 2010 to $2T, 1.2B usersin 2050 The industry sector will dominate the national economy for decades The urbanization rate will increase from 49.7% in 2010 to 80.0% in 2050

  4. 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 Smart curve saw: 25 meters/minute 0.1  0.05 mm saved 6 KW power Polish Machine

  5. 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

  6. Example: Informatization Alexa Top Sites 1. Google2. Facebook3. YouTube4. Yahoo!5. Baidu6. Wikipedia7. Blogger8. Windows Live9. Twitter10. QQ 16. Taobao17. Sina21. eBay • 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

  7. 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

  8. Example of Utilizing Ternary Resources 200 million families’ Electricity consumption behavior （habits, economic incentives, social relationship） Cyber world Human Society Energy Saving: In 2009,an average household in China consumed 1044 KWH But a green households in Beijingonly consumed 600 KWH By 2030, household electricity consumption could be reduced by 30% through sensing and promoting green practices Bill • Automatically sense human society and physical world • Search optimal behavior of electricity consumption • Promote best practices of energy consumption Upgrading Household Appliances: New energy-saving appliances as data-intensive as Rolls-Royce aircraft engines Human meter reading 15 bytes/month electrical appliancesphysical behavior of using electricity 15 GB/month Physical World

  9. Ternary Computing Research Is Starting • 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

  10. Connectivity and Integration of People, Machines, Things

  11. Capability Upgrade through FIT Innovations Informationization Capability Function sensing of physical world and human society Evolvableinternet with end-to-end quality assurance Sea-cloud computing handling ZB scale (1021 bytes) of data FIT Innovations Pervasive intelligent services with Human-Cyber-Physical integration (billions of users, trillions ofdevices) Material Device  Equipment System Acquisition Information Technology Physical parameter sensing Transmission Best-effort packet switched networks Processing Cloud computing handling PBscale (1015 bytes) of data Addressing constraints of power and security Internets of Things, Media, Services (100s million users, billionhosts) Application Informationization

  12. Speed, power, software complexity trendsthe Three 100-million issues 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) 2020

  13. 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 Information reduction Functional Sensing Cognitioninformation Behavior

  14. 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 IPv6 IPv4 Non-IP PEARL routers in one physical networkEach PEARL routers provides 4 Gbps ports and customizable data/ctrl planes, and support 128 virtual routers G. Xie et al, “PEARL: A Programmable Virtual Router Platform”, IEEE Communication, July 2011

  15. 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

  16. Physical Information Media Information Social Information Smart grid, public safety, intelligent traffic systems, etc. Value in productivity, sustainability, welfare and well-being Applications (IIS) Systems (CNS) Foundation (CCF) Functional sensing, customizable internet, sea-cloud computing systems FIT architecture, ternary computing models, security and privacy Phenomena, metrics, laws, abstractions, mechanisms Time/space complexity Energy complexity; effort complexity, sensor complexity Ecosystems Science User Experience Service Application Middleware System Software Machines Components Apple Facebook Google Tencent IBM DEC …… EDS, Andersen, … IBM, Accenture, … MS Office, SAP, … MS Office, SAP, … Oracle, BEA, … Oracle, BEA, LAMP, … Windows, Unix, … Android, Windows, Linux… HP, Cisco, Dell, … HP, Cisco, Lenovo, … Intel, Seagate, … Intel, ARM, Seagate, … 1955-1980Vertical 1980-2005Horizontal 2005-2030End-to-end ecosystems

  17. 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)

  18. 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.

  19. Has user definable microarchitecture that can be changed dynamically to adapt to applications’ requirements 2 orders of magnitude improvement in power efficiency Elastic Processor Current chip design solutions Many US scientists are researching similar issues NSF Expedition project: Customizable Domain-Specific Computing www.cdsc.ucla.edu GreenDroid at UCSD Utilization wall Conservation cores IEEE Micro, 3/4 2011 MOPS/mW ASIC Hardwired solution Elastic Processor FPGA Field and gate-level reconfiguration GPP Software solution Flexibility

  20. Thank you! zxu@ict.ac.cn

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