{"title":"Area recovery under depth constraint by Cut Substitution for technology mapping for LUT-based FPGAs","authors":"Taiga Takata, Y. Matsunaga","doi":"10.1109/ASPDAC.2008.4483928","DOIUrl":null,"url":null,"abstract":"In this paper we present the post-processing algorithm, cut substitution, for technology mapping for LUT-based FPGAs to minimize the area under depth minimum constraint. The problem to generate a LUT's network whose area is minimum under depth minimum constraint seems to be as difficult as NP-hard class problem. Cut substitution is the process to generate a local optimum solution by eliminating redundant LUTs while the depth of network is maintained. The experiments shows that the proposed method derives the solutions whose area are 9% smaller than the solutions of a previous state-of-the-art, DAOmap on average.","PeriodicalId":277556,"journal":{"name":"2008 Asia and South Pacific Design Automation Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Asia and South Pacific Design Automation Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASPDAC.2008.4483928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper we present the post-processing algorithm, cut substitution, for technology mapping for LUT-based FPGAs to minimize the area under depth minimum constraint. The problem to generate a LUT's network whose area is minimum under depth minimum constraint seems to be as difficult as NP-hard class problem. Cut substitution is the process to generate a local optimum solution by eliminating redundant LUTs while the depth of network is maintained. The experiments shows that the proposed method derives the solutions whose area are 9% smaller than the solutions of a previous state-of-the-art, DAOmap on average.