1. Comments on Development-Oriented vs Basic Research Prof. Jason Cong cong@cs.ucla.edu Computer Science Department University of California, Los Angeles

2. Is There Basic Research In Computer Science u What is basic research?  Understanding the nature and discovering the ultimate truth  Without consideration of practical implication or commercial impact u With exception of theory, most of computer science is applied research, not basic research u But there is a lot to leverage from basic research  Examples from my research areas in computer-aided design

3. UCLA VLSICAD LAB Characterization of Programmable Logic Blocks for Lookup-Table (LUT) based FPGAs (Field-Programmable Gate-Arrays) PLB2 24 Memory cells ( > 4 inputs) G H PLB3 28,36 Memory cells ( > 4 inputs) G F H 3,4 Xilinx 4000 CLB 40 Memory cells ( > 5 inputs) G F H H1 PLB1 24-48 Memory cells ( > 4 or 5 inputs) G F 3,4,5 S Each box is a k-input look-up table, can implement of any Boolean function up to k inputs

4. Use of Theory of Functional Decomposition  We ’ d like to answer the questions: f(x 1,x 2, …,x n ) = g(y 1 (x 1, …,x k ), …,y m (x 1, …,x k ),x k+1,x k+2, …,x n )) f(x 1,x 2, …,x n ) = g(y 1 (x 1, …,x k ), …,y m (x 1, …,x k ),x k+1,x k+2, …,x n )) u Ashenhurst Decomposition (1959) u Roth-Karp Decomposition (1962) u Recent improvement using Binary Decision Diagram (BDD)

5. mPL produced best result at ISPD’2006 Placement Contest

6. Multilevel Optimization Framework Interpolation & Relaxation (optimization) Coarsening (Clustering) Problem size decreases Successful in many applications Originally developed for partial differential equations (PDEs) Recent success in VLSI CAD: partitioning, placement, routing Collaboration of Tony Chan (UCLA Math) Given problem

7. How to Best Leverage Basic Research ? u Inter-discipline research is very important  Are we aware what’s going on in basic research?  How and who will bridge the gap? u A broad education of students is very important  Be able to understand and appreciate basic research

8. Short-Term vs. Long-Term Applied Research u Need to focus on long-term applied research  5 -15 years beyond immediate application  Pre-competitive  Example: the invention of Internet 1 st node of arpa-net built at UCLA in 1969 1 st node of arpa-net built at UCLA in 1969 u It is not easy to identify long-term research in an exponential growing field  Law of exponential power: Every generation of advancement is larger than the sum of all previous generations u What takes to do long-term applied research  Vision and good taste  Sufficient funding  Reward and understanding of risk taking