Reversible Logic Synthesis-Oriented Multi-Objective Automatic Design Method Based on Evolutionary Design Techniques

Ming Ming Zhang; Shu Guang Zhao; Xu Wang

doi:10.4028/www.scientific.net/KEM.439-440.534

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 439-440Reversible Logic Synthesis-Oriented...

Reversible Logic Synthesis-Oriented Multi-Objective Automatic Design Method Based on Evolutionary Design Techniques

Abstract:

This paper applies evolutionary design techniques to the reversible logic synthesis, and proposes a reversible logic synthesis-oriented multi-objective automatic design method based on evolutionary design techniques. Firstly, build a gate-level array model of reversible logic circuits (RLC) in order to model the synthesis problems as ones of constrained multi-objective optimizations. Then, encode the candidate RLC to a set of binary evolutionary individuals which are solved by the specialized Pareto-optimal multi-objective evolutionary algorithm. In addition, adopt “pre-bit priority” mechanism to repair infeasible individuals and adopt the rule-based local transformation method to simple the redundant RLC. The results of synthesis experiments demonstrate that the proposed method is feasible and effective, and can automatically synthesize the better RLC.

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Key Engineering Materials (Volumes 439-440)

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534-539

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https://doi.org/10.4028/www.scientific.net/KEM.439-440.534

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June 2010

Authors:

Ming Ming Zhang, Shu Guang Zhao, Xu Wang

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Automatic Design, Multi-Objective Optimization (MOP), Reversible Logic Synthesis

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References

[1] R. Landauer, Irreversibility and heat generation in the computing process, IBM J. Res. Develop., vol. 5, pp.183-191, (1961).

DOI: 10.1147/rd.53.0183

Google Scholar

[2] A. Mishchenko and M. Perkowski, Logic synthesis of reversible wave cascades, in Proc. Int. Workshop on Logic Synthesis, Jun. 2002, pp.197-202.

Google Scholar

[3] V. V. Shende, A. K. Prasad, I. L. Markov, et al, Synthesis of reversible logic circuits, IEEE Trans. Computer-Aided Design Integr. Circuits Syst., vol. 22, no. 6, pp.710-722, (2003).

DOI: 10.1109/tcad.2003.811448

Google Scholar

[4] D. M. Miller and G. W. Dueck, Spectral techniques for reversible logic synthesis, in Proc. 6th Int. Symp. Represent. Methodol. Future Comput. Technol., Mar. 2003, pp.56-62.

Google Scholar

[5] D. Maslov and G. W. Dueck, Reversible cascades with minimal garbage, IEEE Trans. Computer-Aided Design Integr. Circuits Syst., vol. 23, no. 11, pp.1497-1509, (2004).

DOI: 10.1109/tcad.2004.836735

Google Scholar

[6] D. Maslov, G. W. Dueck, D. M. Miller, Toffoli Network Synthesis with Templates, IEEE Trans. Computer-Aided Design Integr. Circuits Syst., vol. 24, no. 6, pp.807-817, (2005).

DOI: 10.1109/tcad.2005.847911

Google Scholar

[7] P. Gupta, A. Agrawal and N. K. Jha, An algorithm for synthesis of reversible logic circuits, IEEE Trans. Computer-Aided Design Integr. Circuits Syst., vol. 25, no. 11, pp.2317-2330, (2006).

DOI: 10.1109/tcad.2006.871622

Google Scholar

[8] K. Deb, A. Pratap, S. Agarwal, et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp.182-197, (2002).

DOI: 10.1109/4235.996017

Google Scholar