Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@InProceedings{Berndt:2021:SBCCI,

• author = "Augusto Berndt and Isac S. Campos and Bryan Lima and Mateus Grellert and Jonata T. Carvalho and Cristina Meinhardt and Brunno A. {De Abreu}",
• title = "Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization",
• booktitle = "2021 34th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)",
• year = "2021",
• month = aug,
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• DOI = "doi:10.1109/SBCCI53441.2021.9529968",
• abstract = "Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Traditional approaches for logic synthesis have been in the spotlight so far. However, due to advances in machine learning and their high performance in solving specific problems, such algorithms appear as an attractive option to improve electronic design tools. In our work, we explore Cartesian Genetic Programming for logic optimization of exact or approximate combinational circuits. The proposed CGP flow receives input from the circuit description in the format of AND-Inverter Graphs and its expected behavior as a truth-table. The CGP may improve solutions found by other techniques used for bootstrapping the evolutionary process or initialize the search from random (unbiased) individuals seeking optimal circuits. We propose two different evaluation methods for the CGP: to minimize the number of AIG nodes or optimize the circuit accuracy. We obtain at least 22.percent superior results when considering the ratio between accuracy and size for the benchmarks used, compared with the teams from the IWLS 2020 contest that obtained the best accuracy and size results. It is noteworthy that any logic synthesis approach based on AIGs can easily incorporate the proposed flow. The results obtained show that their usage may achieve improved logic circuits.",
• notes = "Also known as \cite{9529968} See \cite{Berndt:2022:JICS}",

}

Genetic Programming entries for Augusto Andre Souza Berndt Isac de Souza Campos Bryan Martins Lima Mateus Grellert Jonata Tyska Carvalho Cristina Meinhardt Brunno A de Abreu

Citations