Abstract
A new approach to the evolutionary optimization of large digital circuits is introduced in this paper. In contrast with evolutionary circuit design, the goal of the evolutionary circuit optimization is to minimize the number of gates (or other non-functional parameters) of already functional circuit. The method combines a circuit simulation with a formal verification in order to detect the functional inequivalence of the parent and its offspring. An extensive set of 100 benchmarks circuits is used to evaluate the performance of the method as well as the utilized evolutionary approach. Moreover, the role of neutral mutations in the context of evolutionary optimization is investigated. In average, the method enabled a 34 % reduction in gate count even if the optimizer was executed only for 15 min.
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ABC is a system for sequential synthesis and verification by A. Mishchenko.
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A PC equipped with Intel Xeon X5670 (24 cores, 2.93 GHz, 12 MB cache), 32 GB RAM and 64-bit CentOS Linux was used.
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Acknowledgments
This work was supported by the Czech science foundation project 14-04197S.
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Vasicek, Z. (2015). Cartesian GP in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gates. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_12
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DOI: https://doi.org/10.1007/978-3-319-16501-1_12
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