EA-based resynthesis: an efficient tool for optimization of digital circuits

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@Article{Kocnova:GPEM:resynthesis,

• author = "Jitka Kocnova and Zdenek Vasicek",
• title = "{EA-based} resynthesis: an efficient tool for optimization of digital circuits",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2020",
• volume = "21",
• number = "3",
• pages = "287--319",
• month = sep,
• note = "Special Issue: Highlights of Genetic Programming 2019 Events",
• keywords = "genetic algorithms, genetic programming, evolvable hardware, Cartesian genetic programming, Evolutionary resynthesis, Logic optimization",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-020-09376-3",
• size = "33 pages",
• abstract = "Since the early 1990 nineties the lack of scalability of fitness evaluation has been the main bottleneck preventing the adoption of evolutionary algorithms for logic circuits synthesis. Recently, various formal approaches such as SAT and BDD solvers have been introduced to this field to overcome this issue. This made it possible to optimise complex circuits consisting of hundreds of inputs and thousands of gates. Unfortunately, we are facing another problem: scalability of representation. The efficiency of the evolutionary optimization applied at the global level deteriorates with the increasing complexity. To overcome this issue, we propose to apply the concept of local resynthesis in this work. Local resynthesis is an iterative process based on the extraction of smaller sub-circuits from a complex circuit that are optimized locally and implanted back to the original circuit. When applied appropriately, this approach can mitigate the problem of scalability of representation. Two complementary approaches to",

}

Genetic Programming entries for Jitka Kocnova Zdenek Vasicek

Citations