Evolutionary Heuristics for Multiobjective VLSI Netlist Bi-Partitioning

1. Evolutionary Heuristics for Multiobjective VLSI Netlist Bi-Partitioning • by • Dr. Sadiq M. Sait • Dr. Aiman El-Maleh • Mr. Raslan Al Abaji • Computer Engineering Department

2. Outline …. • Introduction • Problem Formulation • Cost Functions • Proposed Approaches • Experimental results • Conclusion

3. 7.5M333MHz0.25um VLSI Technology Trend Design Characteristics 3.3M200MHz0.6um 1.2M50MHz0.8um 0.13M12MHz1.5um 0.06M2MHz6um Cycle-basedsimulation,FormalVerification Top-DownDesign,Emulation HDLs, Synthesis CAESystems, Siliconcompilation Key CAD Capabilities SPICE Simulation The Challenges to sustain such an exponential growth to achieve gigascale integration have shifted in a large degree, from the process of manufacturing technologies to the design technology.

4. The VLSI Chip in 2006 Technology 0.1 um Transistors 200 M Logic gates 40 M Size 520 mm2 Clock 2 - 3.5 GHz Chip I/O’s 4,000 Wiring levels 7 - 8 Voltage 0.9 - 1.2 Power 160 Watts Supply current ~160 Amps Performance Power consumption Noise immunity Area Cost Time-to-market Tradeoffs!!!

5. VLSI Design Cycle • VLSI design process is carried out at a number of levels. • System Specification • Functional Design • Logic Design • Circuit Design • Physical Design • Design Verification • Fabrication • Packaging Testing and Debugging

6. Physical Design The physical design cycle consists of: • Partitioning • Floorplanning and Placement • Routing • Compaction Physical Design converts a circuit description into a geometric description. This description is used to manufacture a chip.

7. Why we need Partitioning ? • Decomposition of a complex system into smaller subsystems. • Each subsystem can be designed independently speeding up the design process (divide-and conquer-approach). • Decompose a complex IC into a number of functional blocks, each of them designed by one or a team of engineers. • Decomposition scheme has to minimize the interconnections between subsystems.

8. Levels of Partitioning System System Level Partitioning PCBs Board Level Partitioning Chips Chip Level Partitioning Subcircuits / Blocks

9. Need for Power optimization • Portable devices. • Power consumption is a hindrance in further integration. • Increasing clock frequency. • Need for delay optimization • In current sub micron design wire delay tend to dominate gate delay. Larger die size imply long on-chip global routes, which affect performance. • Optimizing delay due to off-chip capacitance. Motivation

10. Objective • Design a class of iterative algorithms for VLSI multi objective partitioning. • Explore partitioning from a wider angle and consider circuit delay , power dissipation and interconnect in the same time, under balance constraint.

11. Problem formulation • Objectives : • Power cost is optimized AND • Delay cost is optimized AND • Cutset cost is optimized • Constraint • Balanced partitions to a certain tolerance degree. (10%)

12. cutset = 3 Cutset • Based on hypergraph model H = (V, E) • Cost 1: c(e) = 1 if e spans more than 1 block • Cutset = sum of hyperedge costs • Efficient gain computation and update

13. Delay Model path : SE1 C1C4C5SE2. Delay = CDSE1 + CDC1+ CDC4+ CDC5+ CDSE2 CDC1 = BDC1 + LFC1 * ( Coffchip + CINPC2+ CINPC3+ CINPC4)

14. Delay Delay(Pi) = Delay(Pi) = Pi: is any path Between 2 cells or nodes P : set of all paths of the circuit.

15. Power The average dynamic power consumed by CMOS logic gate in a synchronous circuit is given by: Ni : is the number of output gate transition per cycle( switching Probability) : Is the Load Capacitance

16. Power :Load Capacitances driven by a cell before Partitioning : additional Load due to off chip capacitance.( cut net) Total Power dissipation of a Circuit:

17. Power : Can be assumed identical for all nets :Set of Visible gates Driving a load outside the partition.

18. Balance The Balance as constraint is expressed as follows: However balance as a constraint is not appealing because it may prohibits lots of good moves. Objective : |Cells(block1) – Cells( block2)|

19. Fuzzy logic for cost function • Imprecise values of the objectives • best represented by linguistic terms that are basis of fuzzy algebra • Conflicting objectives • Operators for aggregating function

20. Use of fuzzy logic for Multi-objective cost function • The cost to membership mapping. • Linguistic fuzzy rule for combining the membership values in an aggregating function. • Translation of the linguistic rule in form of appropriate fuzzy operators.

21. Some fuzzy operators • And-like operators • Min operator  = min (1, 2) • And-like OWA •  =  * min (1, 2) + ½ (1- ) (1+ 2) • Or-like operators • Max operator  = max (1, 2) • Or-like OWA •  =  * max (1, 2) + ½ (1- ) (1+ 2) • Where  is a constant in range [0,1]

22. Membership functions Where Oiand Ciare lower bound and actual cost of objective “i” i(x) is the membership of solution x in set “good ‘i’ ” giis the relative acceptance limitfor each objective.

23. Fuzzy linguistic rule • A good partitioning can be described by the following fuzzy rule • IFsolution has • small cutsetAND • low powerAND • short delay AND • good Balance. • THENit is a good solution

24. Fuzzy cost function The above rule is translated to AND-like OWA Represent the total Fuzzy fitness of the solution, our aim is to Maximize this fitness. Respectively (Cutset, Power, Delay , Balance ) Fitness.

25. GA for multiobjective Partitioning Algorithm (Genetic_Algorithm) Construct_Population(Np); For j = 1 to Np Evaluate_Fitness (Population[j]) End For; For i = 1 to Ng For j = 1 to No (x,y) Choose_parents; offspring[j] Crossover(x,y) EndFor; Population  Select ( Population, offspring, Np ) For k = 1 to Np Apply Mutation (Population[k]) EndFor; EndFor;

26. Solution representation

27. GA implementation • Different population sizes • Parent selection: Roulette wheel • The probability of selecting a chromosome for crossover is • Np is the population size

28. GA implementation • Simple single point • crossover: • Selection before mutation • Roulette wheel (rlt) • Elitism random (ernd)

29. Tabu Search • Algorithm Tabu_Search •  Start with an initial feasible solution S  • Initialize tabu list and aspiration level • For fixed number of iterations Do • Generate neighbor solutions V*  N(S) • Find best S*  V* • If move S to S* is not in T Then • Accept move and update best solution • Update T and AL • Else If Cost(S*) < AL Then • Accept move and update best solution • Update T and AL • End If • End For

30. TS implementation • Neighbor solution • Change the block of a randomly selected cells. • The Tabu list size depends on the circuit size.

31. TS implementation • Tabu list • Store index of one of the swapped cell. • Various sizes for tabu list. • Aspiration Level • The best neighbor is better than the global best.

32. Experimental Results ISCAS 85-89 Benchmark Circuits

33. GA Vs Tabu Multi-objective

34. Circuit S13207 GA

35. Circuit S13207 TS

36. Circuit S13207 GA Vs TS time

37. Conclusion • The present work successfully addressed the important issue of reducing power and delay consumption in VLSI circuits. • The present work successfully formulate and provide solutions to the problem of multiobjective VLSI partitioning • TS partitioning algorithm outperformed GA in terms of quality of solution and execution time

38. Thank you.