Resynthesis of logic circuits using machine learning and reconvergent paths

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Kocnova:2021:DSD,

• author = "Jitka Kocnova and Zdenek Vasicek",
• title = "Resynthesis of logic circuits using machine learning and reconvergent paths",
• booktitle = "2021 24th Euromicro Conference on Digital System Design (DSD)",
• year = "2021",
• pages = "69--76",
• address = "Palermo, Italy",
• month = "1-3 " # sep,
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Scalability, Logic circuits, Machine learning, Logic gates, Benchmark testing, Tools, Genetics, Logic optimization, Evolutionary Resynthesis",
• isbn13 = "978-1-6654-2704-3",
• DOI = "doi:10.1109/DSD53832.2021.00020",
• size = "8 pages",
• abstract = "Boolean network scoping represents a common approach incorporated in conventional synthesis tools for maintaining good scalability of the synthesis process. Recently, an approach to the local resynthesis based on combination of evolutionary optimization with the principle of Boolean network scoping has been proposed. 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. The main advantage of the local resynthesis is that it can mitigate the problem of scalability of representation which is typical to the evolutionary algorithms as the efficiency of the evolutionary optimization applied at the global level deteriorates with the increasing circuit complexity. Unfortunately, the efficiency of local resynthesis depends on the efficiency of the sub-circuit extraction process. We propose an alternative method, based on the reconvergent paths. The evaluation is performed on a set of highly optimized benchmark problems representing various real-world controllers, logic and arithmetic circuits. The method provides better results compared to the state-of-the-art logic synthesis tool and evolutionary optimization techniques operating locally and globally. A substantially higher number of redundant gates was removed in more than 70percent cases, while keeping the computational effort at the same level. A huge improvement was achieved especially for the controllers. On average, the proposed method was able to remove more than 14.3percent of gates. The highest achieved gate reduction was more than 45percent of gates.",
• notes = "Also known as \cite{9556495}",

}

Genetic Programming entries for Jitka Kocnova Zdenek Vasicek

Citations