NSF Workshop Electronic Design Automation Past, Present, and Future July 8-9, 2009

1. NSF WorkshopElectronic Design AutomationPast, Present, and FutureJuly 8-9, 2009 Sankar Basu, Robert Brayton, and Jason Cong,

2. Purpose This workshop was organized • to reflect on the success of EDA to see how • its practice can influence other fields of computer science, and • its methodology can be applied to other application domains, and • to review the progress made under the National Design Initiative and evaluate what new directions and topics should be added to the Initiative. • Counteract the notion that it is only engineering • Clarify to outsiders what EDA is

3. Organization First Day –series of talks covering broad areas of EDA and selected emerging technologies that might benefit from EDA methodologies. Second Day – • broke up into focus groups • EDA Past, Present and Future Support. • Funding of EDA, Research Opportunities and Interaction with Industry. • EDA for Emerging/Adjacent Technologies. • Educational Aspects. • EDA and Theory. • groups answered focused questions and prepared summaries • reconvened to hear summaries Follow up – Groups prepared extensive reports which were merged into a final report http://cadlab.cs.ucla.edu/nsf09/

4. Keynote Talks Ralph Cavin and Bill Joyner (SRC), and Wally Rhines (Mentor Graphics) Prith Banerjee (HP) Invited Talks Sharad Malik, Princeton Andreas Kuehlmann, Cadence Arvind, MIT Jochen A. G. Jess, Eindhoven University (emeritus) Carl Seger, Intel Corp. Edmund M. Clarke, CMU Shaz Qadeer, Microsoft Tim Cheng, UC Santa Barbara Rupak Majumdar, UC Los Angeles Jaijeet Roychowdhury, UC Berkeley Rob A. Rutenbar, CMU Jason Hibbeler, IBM Jyuo-Min Shyu, National Tsing Hua University Igor Markov, University of Michigan Mary Jane Irwin, Penn State David Z. Pan, UT Austin Jim Heath, Caltech Chris Myers, University of Utah Lou Scheffer, Howard Hughes Medical Institute Talks abstracts and .ppt slides of talks - http://cadlab.cs.ucla.edu/nsf09/

5. Keynote Talks The Brave New Old World of Design Automation Research, Ralph Cavin Bill Joyner Wally Rhines Future IT Infrastructure Research Challenges: An HP Labs View, Prith Banerjee Invited Talks The Future of Electronic Design Automation: Methodology, Tools and Solutions, Sharad Malik, EDA - Electronic Design Automation or Electronic Design Assistance?, Andreas Kuehlmann, Front-end SoC design: The Neglected Frontier, Arvind EDA Challenges in Systems Integration, Jochen A. G. Jess Is Today’s Design Methodology a Recipe for a "Tacoma Narrows" Incident?, Carl Seger Statistical Model Checking of Simulink Models, Edmund M. Clarke Deconstructing Concurrency Heisenbugs, Shaz Qadeer Test and Validation Challenges in the Late-Silicon Era, Tim Cheng A Faulty Research Agenda, Rupak Majumdar Numerical Modeling and Simulation for EDA: Past, Present and Future, Jaijeet Roychowdhury ANALOG CAD: NOT DONE YET, Rob A. Rutenbar A Flat Earth for Design and Manufacturing, Jason Hibbeler Collaborative Innovation of EDA, Design, and Manufacturing, Jyuo-Min Shyu From Computability to Simulation, Optimization, and Back, Igor Markov Working Around the Limits of CMOS, Mary Jane Irwin More Moore’s Law Through Computational Scaling - and EDA’s Role, David Z. Pan Robotics-Based Fabrication and Assay Automation for In Vitro Diagnostics Technologies, Jim Heath Synthetic Biology: A New Application Area for Design Automation Research, Chris Myers EDA and Biology of the Nervous System, Lou Scheffer

6. What is EDA? • methodologies, algorithms and tools, which assist and automate the design, verification, and testing of electronic systems. • a general methodology for refining a high-level description down to a detailed physical implementation for designs ranging from • integrated circuits (including system-on-chips), • printed circuit boards (PCBs) and • electronic systems. • the modeling, synthesis, and verification at every level of abstraction.

7. Foundational Areas • Verification/validation, model checking, and testing • Synthesis (logical and physical) research • Programming language research • Analog and mixed signal design • Non-linear model reduction

8. Key EDA challenges • Scalable design methodologies • synthesis, • validation/verification • New classes of algorithms for scalability • Linear/sub-linear algorithms. • Incremental algorithms • Parallel algorithms. • Deterministic algorithms for parallel programs • Design for security – resilient to attacks • Dealing with new technologies • Designing with uncertainty and fragility

9. EDA Funding comparisons NSF funding of academic EDA research • Computer & Information Science & Engineering (CISE) ($8M-$12M) • Electrical, Communications and Cyber Systems (ECCS) ($1-$3M) SRC funding of EDA • $5M/year SRC and DARPA Focus Research Centers – EDA part • $4-5M/year TOTAL – $18M-$25M/year

10. Total NSF funding in related areas • CISE - $574M/year • ENG - $693M/year • Electrical Communication and Cyber - $125M/year • Cyber Infrastructure - $199M/year Total – $898M/year (EDA part ~1.3-2%)

11. Funding Comparisons Taiwan • SoC $70M/year (~35M is EDA support) • Telecommunications $70M/year • Nanoelectronics $100M/year Total $240M/year • EDA part? – 35M+/year * academic grants have only a 5% overhead.

12. Funding Comparisons Europe • information and communication technology (ICT) - 1500M Euro/year • nanosciences, nanotechnologies, materials and new production technologies - 575M Euro/year • Electronics, Microelectronics part of EUREKA Consortium – 310M Euro/year • Cluster for Application and Technology Research in Europe on NanoElectronics – 750M Euro/year • ENIAC-JRT (500M Euro/year) supported 15 EDA projects Total – Europe – 3,635Euro/year = $5,452M/year • EDA part?

13. Emerging Areas and EDA technology • Biology systems • System biology • Synthetic biology • Emerging computing /communication /storage fabrics and manufacturing substrates • Nano and flexible electronics • Analysis, characterization, and potential design of hybrid electronic/biological systems. • Bio-neural systems and readouts • Cyber-physical systems. • Smart systems, real time • Datacenter design and optimization. • Energy and reliability in a dynamic workload • Software • Concurrency and scalability

14. Educational Challenges EDA is very broad • what to teach • how to teach it • when to teach it Need to attract more students

15. Current EDA Climate • Many EDA companies are hurting financially, and • job opportunities are down. • EDA summer internships are very tight. • Venture capital for start-ups in EDA has decreased significantly. • have served as major centers for research and development and employment of PhDs. • Faculty positions in EDA are tight, • Difficulty in obtaining funding to support research and students. • Student interest in EDA as a career has decreased in recent years. • reduced industrial research efforts in EDA • large system design companies have throttled back on the research components of their activities. • Transition of academic research to industry is much harder than before. • technologies are more complex • harder to get new ideas into the sophisticated and mature software offered by EDA vendors.

16. Some Good News • EDA will not go away and cannot stagnate. • Cooperation between industry researchers and developers and university faculty and students remains very high • As technology shrinks, the problems get harder, so not less but more EDA activity is required. • EDA engineers are well paid, apparently better than most other types of engineers. • EDA training in its various disciplines, including complex and large problem solving, will be valuable as new growth areas come into • Aside from the new emerging hot areas, EDA continues with its own hot areas, • system-level design • embedded software • design for manufacturing including lithographic and scaling problems • issues of robustness and unreliable components • parallelism, design and application of many core processors • application of probabilistic methods to enhance scaling of algorithms • new methods for derivative and incremental design.

17. Recommendations to NSF Research Programs – new funding for: • mid-scale or large-scale research efforts that couple design with EDA. • joint research programs between research groups from universities, commercial EDA companies, and large systems-houses. • shared infrastructure for design and design automation. • joint exploration of DA for emerging areas. • cyber-physical systems. • architecture and networking programs for data center design and optimization. • software analysis • scalable and more precise large-scale analysis, • tools and methodologies to extract and manage concurrency. • system biology and synthetic biology. • DA for emerging computing/communications/storage fabrics and manufacturing substrates (with Engineering Directorate) • interaction between • DA and theory communities, • DA and mathematical sciences.

18. Recommendations to NSF Education Programs • Support for the development of a senior level EDA course. • emphasize the underlying algorithmic and theoretic foundations of EDA • motivate EDA’s breadth and flexibility with specific interesting applications. • materials broadly submitted by many faculties • materials available online. • Support from NSF to develop shared courseware infrastructure in EDA. • Might utilize connexions (cnx.org), an open platform for course sharing. • An increased post-doc program to alleviate the lack of research positions for new graduates. • such a program was perhaps part of the stimulus effort, but quite limited and not specific to EDA.

19. Recommendations to NSF Collaboration with Industry • An enhanced program to support longer-term faculty/industry interactions. • seeded by enhanced faculty stays in industry • visits by technical leaders from industry to academia. • enabled by matching NSF and industry contributions. • in Engineering Directorate there is a GOALI program • similar program is needed for CISE. • An enhanced program to support EDA students working summers at companies. • students physically at the company. • proposals would be joint effort between a faculty member and a company staff person • could include small start-ups. • A program to help faculty members and graduate researchers spin off start-ups to commercialize successful research projects. • similar to an SBIR program but more focused on EDA. • help cross over from a research paper or prototype to first customer adoption, • then VCs or the large EDA companies could take over from there. • A program to help marry faculty to existing start-ups (related to the above). • encourage new ventures in EDA-type activities.

20. Estimated Cost of Recommendations • $10-15M/year NEW funding • Shared with engineering directorate

21. More Information See http://cadlab.cs.ucla.edu/nsf09/ for both the talks (titles and slides) and report.