Adaptive verifiability-driven strategy for evolutionary approximation of arithmetic circuits

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@Article{CESKA:2020:ASC,

• author = "Milan Ceska and Jiri Matyas and Vojtech Mrazek and Lukas Sekanina and Zdenek Vasicek and Tomas Vojnar",
• title = "Adaptive verifiability-driven strategy for evolutionary approximation of arithmetic circuits",
• journal = "Applied Soft Computing",
• volume = "95",
• pages = "106466",
• year = "2020",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2020.106466",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494620304063",
• keywords = "genetic algorithms, genetic programming, Approximate computing, Energy efficiency, Circuit optimisation",
• abstract = "We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing formal guarantees on the approximation error into an evolutionary circuit optimisation algorithm. The key idea is to employ a novel adaptive search strategy that drives the evolution towards promptly verifiable approximate circuits. As demonstrated in an extensive evaluation including several structurally different arithmetic circuits and target precisions, the search strategy provides superior scalability and versatility with respect to various approximation scenarios. Our approach significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably-correct circuit approximations",

}

Genetic Programming entries for Milan Ceska Jiri Matyas Vojtech Mrazek Lukas Sekanina Zdenek Vasicek Tomas Vojnar

Citations