{"title":"一种有效的多输出MPRM电路的优化算法","authors":"Dengli Bu, Jianhui Jiang","doi":"10.1109/ITAIC.2014.7065040","DOIUrl":null,"url":null,"abstract":"By incorporating cube transformation and local transformation, a heuristic algorithm is proposed for MPRM (mixed-polarity Reed-Muller) logic optimization. The proposed algorithm can be applied to multi-output circuits with very large number of input variables, and can adaptively decide to use cube transformation or local transformation during the optimization process. The proposed algorithm is implemented in C language and tested by using several MCNC and IWLS'93 benchmark circuits with many input and output variables. Experimental results show that, in comparison with other algorithms, the proposed algorithm can obtain good optimized results and can significantly improve the time efficiency of MPRM optimization.","PeriodicalId":111584,"journal":{"name":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An efficient optimization algorithm for multi-output MPRM circuits with very large number of input variables\",\"authors\":\"Dengli Bu, Jianhui Jiang\",\"doi\":\"10.1109/ITAIC.2014.7065040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By incorporating cube transformation and local transformation, a heuristic algorithm is proposed for MPRM (mixed-polarity Reed-Muller) logic optimization. The proposed algorithm can be applied to multi-output circuits with very large number of input variables, and can adaptively decide to use cube transformation or local transformation during the optimization process. The proposed algorithm is implemented in C language and tested by using several MCNC and IWLS'93 benchmark circuits with many input and output variables. Experimental results show that, in comparison with other algorithms, the proposed algorithm can obtain good optimized results and can significantly improve the time efficiency of MPRM optimization.\",\"PeriodicalId\":111584,\"journal\":{\"name\":\"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITAIC.2014.7065040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITAIC.2014.7065040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An efficient optimization algorithm for multi-output MPRM circuits with very large number of input variables
By incorporating cube transformation and local transformation, a heuristic algorithm is proposed for MPRM (mixed-polarity Reed-Muller) logic optimization. The proposed algorithm can be applied to multi-output circuits with very large number of input variables, and can adaptively decide to use cube transformation or local transformation during the optimization process. The proposed algorithm is implemented in C language and tested by using several MCNC and IWLS'93 benchmark circuits with many input and output variables. Experimental results show that, in comparison with other algorithms, the proposed algorithm can obtain good optimized results and can significantly improve the time efficiency of MPRM optimization.