A. Yoshida, T. Miki, M. Shimada, Yuri Yoneda, J. Shirakashi
{"title":"Experimental Evaluation of Performance Improvement by Sparse Operation in Ising Spin Computing","authors":"A. Yoshida, T. Miki, M. Shimada, Yuri Yoneda, J. Shirakashi","doi":"10.1109/NMDC46933.2022.10052337","DOIUrl":null,"url":null,"abstract":"The ability to quickly solve combinatorial optimization problems is essential for improving society and industry. For solving the problems, we present extraction-type majority voting logic (E-MVL) that purposely discards the interaction between the spins by scheduling a parameter, called sparsity. In this paper, the intrinsic computation time of E-MVL is estimated by using step-to-solution (STS) which evaluates the performance independent of implementation. We show that the E-MVL can explore the ground state to the Sherrington-Kirkpatrick model essentially faster than highly optimized simulated annealing (SA). These results indicate that E-MVL is more effective for optimization problems than SA.","PeriodicalId":155950,"journal":{"name":"2022 IEEE Nanotechnology Materials and Devices Conference (NMDC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Nanotechnology Materials and Devices Conference (NMDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NMDC46933.2022.10052337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The ability to quickly solve combinatorial optimization problems is essential for improving society and industry. For solving the problems, we present extraction-type majority voting logic (E-MVL) that purposely discards the interaction between the spins by scheduling a parameter, called sparsity. In this paper, the intrinsic computation time of E-MVL is estimated by using step-to-solution (STS) which evaluates the performance independent of implementation. We show that the E-MVL can explore the ground state to the Sherrington-Kirkpatrick model essentially faster than highly optimized simulated annealing (SA). These results indicate that E-MVL is more effective for optimization problems than SA.