VLSI SystemsSpring 2009 Introduction syllabus goals schedule project

VLSI Systems--Spring 2009 Introduction: --syllabus; goals --schedule --project --student survey, group formation

Hardware Design Space Issues: --timing --synch/asynch --parallelism --digital/analog --space --power

“Optimal” processor: speed size I/O power cost flexibility (parameterization, new technology) ………

Designing the processor: --basic design --parameterization --valid experiments

Processor levels: --system specification --architecture (modules) --logic level --(physical level)

Design process: Linear Sequential Model (“waterfall model”): Sequential approach from system level through analysis, design, coding, testing, support-oldest and most widely used paradigm Analysis Design Code Test Modern design is incremental: top level— requirements, specify, design for test Maintain implement, TEST implement, test ……………………… lowest level: req, spec, design for test, IMPLEMENT, TEST

Example: A System Factors (Experimental Conditions) System Inputs System (“Black Box”) System Outputs Responses (Experimental Results)

Experimental Research Define System Identify Factors and Levels Define system outputs first ● Then define system inputs ● Finally, define behavior (i. e. , transfer function) ● Identify system parameters that vary (many) ● Reduce parameters to important factors (few) ● Identify values (i. e. , levels) for each factor ● Identify Response(s) ● Identify time, space, etc. effects of interest Design Experiments ● Identify factor-level experiments

Introduction--The Three Faces of the Experimenter I have to try every combination of problem instance and treatment. I’ll NEVER meet the conference deadline. I tried my treatment on one carefully chosen problem instance. It MUST be the best treatment. Problem Instance / Treatment Space I used well-established statistical techniques and design of experiments to minimize cost of the experiments and to maximize confidence in the results.

Create and Execute System; Analyze Data Define Workload Create System Execute System Analyze & Display Data Workload can be a factor (but often isn't) ● Workloads are inputs that are applied to system ● Create system so it can be executed ● Real prototype ● Simulation model ● Empirical equations ● Execute system for each factor-level binding ● Collect and archive response data ● Analyze data according to experiment design ● Evaluate raw and analyzed data for errors ● Display raw and analyzed data to draw conclusions ●

Some Examples Analog Simulation – Which of three solvers is best? – What is the system? – Responses • Fastest simulation time • Most accurate result • Most robust to types of circuits being simulated – Factors • Solver • Type of circuit model • Matrix data structure Epitaxial growth – New method using nonlinear temp profile – What is the system? – Responses • Total time • Quality of layer • Total energy required • Maximum layer thickness – Factors • Temperature profile • Oxygen density • Initial temperature • Ambient temperature

SUMMARY— 15 IMPORTANT POINTS FOR EXPERIMENTERS: 1. Even careful experimentation and observation may miss important facts; new experiments may cause old conclusions to be discarded; EXPERIMENTS ARE NOT PROOFS. 2. It is just as important to report NEGATIVE results as to report POSITIVE results. The experimenter must always accurately record and thoroughly report ALL results. 3. IGNORING IMPORTANT FACTORS CAN LEAD TO ERRONEOUS CONCLUSIONS, SOMETIMES WITH TRAGIC RESULTS. 4. YOUR RESULTS ARE ONLY VALID FOR THE PART OF THE DATA-TREATMENT SPACE YOU HAVE EXPLORED; YOU CANNOT CLAIM KNOWLEDGE OF WHAT YOU HAVE NOT EXPLORED 5. An experiment is worthless unless it can be REPEATED by other researchers using the same experimental setup; experimenters have a duty to the research community to report enough about their experiment and data so that other researchers can verify their claims 6. YOU ONLY GET ANSWERS TO THE QUESTIONS YOU ASK 7. if your are going to use a (pseudo-)RANDOM NUMBER GENERATOR, make sure the output behaves enough like a sequence of TRUE RANDOM NUMBERS 8. An experiment must be repeated a SUFFICIENT NUMBER OF TIMES for the results to be attributed to more than random error 9. Choosing the CORRECT MEASURE for the question you are asking is an important part of the experimental design 10. Reporting CORRECT results, PROPERLY DISPLAYED, is an integral part of a well-done experiment 11. MISUSE OF GRAPH LABELING can lead to MISLEADING RESULTS AND INCORRECT CONCLUSIONS 12. INTERPOLATING your results to regions you have not explored can lead to INCORRECT CONCLUSIONS 13. IGNORING the “NULL HYPOTHESIS” when reporting your results can be very misleading 14. Don’t mistake CORRELATION for DEPENDENCE 15. Justify your choice of CURVE using VALID STATISTICS, not “appearance”

Also important: Future technology example: itrs roadmap