1. Behnam Ghavami and Hossein Pedram Presented by Wei-Lun Hung A CAD Framework for Leakage Power Aware Synthesis of Asynchronous Circuits

2. Outline Introduction AsyncTool: Synthesis of QDI Asynchronous Circuits Statistic Performance Analyzing Transistor’s Parameters Assignment Experimental Results Conclusion

3. Introduction The VLSI design challenges High power consumption Synchronization problems Robust issues One possible solution: Asynchronous circuit Low power consumption No clock skew Low Electromagnetic Interference (EMI)

4. Asynchronous Circuits Not controlled by global clock Eliminate clock skew Potentially faster Low power consumption Low EMI Rely on exchanging handshaking Limitations Lack of automatic synthesis tool Hard to evaluate performance of asynchronous circuits

5. Transistor’s Parameters The Vth, Vdd and gate size are the parameters which affect the performance of circuits Heuristically search to find a good tradeoff according to the optimization goal The optimization of synchronous circuits Multiple-Vth and multiple-Vdd assignment Ex: the gates on critical paths operate at the higher Vdd or lower Vth The optimization asynchronous circuits Cannot compute a critical path as synchronous circuits Depends on dynamic factors, ex: # of tokens

6. The Framework of Asynchronous Circuit

7. AsyncTool: Synthesis of QDI Asynchronous Circuits

8. Asynchronous Circuit Model Delay-insensitive (DI) Most robust of all asynchronous circuit delay models Makes no assumptions on the delay of wires or gates Any transition on an input to a gate must be seen on the output Not practical due to the heavy restrictions Quasi delay-insensitive (QDI) Like DI, but Assume that the delay of the branch are equal (isochronic forks) Use Verilog-CSP Code in this framework

9. AsyncTool: Synthesis of QDI Asynchronous Circuits Use Pre-Charge logic Full-Buffer (templates) for its predefined templates Encapsulate all isochronic forks inside Eliminate isochronic fork constrain 3 Parts Arithmetic function extractor (AFE) Ex: Addition, subtraction, comparison... Implements them with pre-synthesized standard templates Decomposition Template Synthesizer (TSYN) one-bit operators, ex: AND, OR, XOR, … Expander is used to convert multiple-bit expressions

10. Decomposition (1/2) Decompose the original description into an equivalent collection of smaller interacting processes Convert to dynamic single assignment form Projection Dynamic Single Assignment form

11. Decomposition (2/2) Projection Break the program up into a concurrent system of smaller modules

12. Statistic Performance Analyzing

13. Petri-Nets Used to model concurrency and synchronization Represented as a bipartite graph Defined as four-tuple N = (P, T, F, m 0 ) P : Set of places T : Se qt of Transitions F ⊆ (P × T) ∪ (T × P) : Flow relation m 0 : Initial marking A Masking is a mapping M: P → N

14. Petri-Nets Examples

15. Timed Petri-Net A Petri-Net in which transitions or places are annotated with delays For a cycle C k, the cycle metric is CM(C k ) = D(C k )/M(C k ) D(C k ) = ∑d i, ∀ i ∈ C k The performance of a Timed Petri-Net is dictated by the cycle time  largest cycle metric C Time = MAX[CM(C k )], ∀ C k ∈ TPN Can be resolved by Maximum Mean-Cycle Algorithms

16. Average Case VS Worst Case

17. Probabilistic Timed Petri-Net

18. The Average-Case Performance Metric For a P-TPN has only one choice with n outcomes Convert to n TPN models For a P-TPN has more than one choice Recursively the following formula

19. Probability Model Use the static range of the primary inputs of the circuit to determine the static range or internal signals Independent VS dependent

20. Computing the Static Range (1/3) The tagged static ranges of a variable v is shown by TSR(v), where r ∈ TSR(v) is expressed as r.ct: the conditional tag r.vt: the variable expression tag r.sr: the static range

21. Computing the Static Range (2/3) Having the static range of the right hand side variables can compute the static range or left hand side variable by Where ° is a standar operator on data values and is operation on static ranges

22. Computing the Static Range (3/3) For a loop

23. Computing Choice Probabilities(1/3) For a condition variable CV(X>Y)

24. Computing Choice Probabilities(2/3)

25. Computing Choice Probabilities(3/3)

26. Template’s Parameters Assignment The Vth, Vdd and gate size are the parameters which affect the performance of circuits Dual-Vdd, dual-Vth and eight sizes for each type of template Adopt Quantum genetic algorithm

27. The Genetic Algorithm A search technique used in computing to find exact or approximate solutions to optimization Use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover Population: abstract representations of candidate solutions Repopulation: generate a second generation population of solutions from those selected through genetic operators Fitness function: decide the surviving chance of individuals

28. The Quantum Genetic Algorithm The circuit configuration information is encoded into qubit A qubit may be in ‘1’ or ‘0’ state, or in any superposition of the two, represented as Ψ 〉 =α1 〉 +β0 〉, where α 2 + β 2 = 1, give the probability that the qubit will be found in ‘0’ or ‘1’

29. The Quantum Genetic Algorithm The population of m qubit individals at generation g is denoted as Q(g) = {q 1 g, q 2 g, …, q n g }, where q j is defined as

30. The Update Procedure

31. The Quantum Genetic Algorithm

32. Fitness Function Power The leakage of a template depends on the number of transistors that re turned off under inputs Calculate the gate leakage under each input pattern Area A qubit have little chance to survival if its area is larger than the area constraint Performance

33. Control Parameters Population size For a small population, the genetic diversity may not increase for many generations For a large population, it may increase the computing time but take fewer generation to find the best solutions Small population of size 10 to 15 perform very well Termination condition The power reduction is less than 0.0005% during the last 200 generations

34. Performance Estimation Results

35. Power Optimization Results

37. Different Technique Comparisons

39. Comparison to worst-case optimized circuits

40. Conclusion An efficient design framework for optimizing reducing total power consumption while maintaining the high performance of circuits Use Probabilistic Timed Petri-Net model to capture the dynamic behavior of the system The proposed assigning threshold-voltage, supply-voltage and template sizing method is based on a quantum genetic algorithm 5X ~ 7X savings for power consumptions with 2.5% performance penalty

41. Comments Not Scalable? Have to specify the static range of the inputs of the circuits The connection between synthesis and parameter assigning is not strong Experimental results are questionable Many typos