{"title":"时间复用FPGA分区中调度压缩的图论优化算法","authors":"Huiqun Liu, D. F. Wong","doi":"10.1109/ICCAD.1999.810683","DOIUrl":null,"url":null,"abstract":"Presents an optimal algorithm to solve the schedule compression problem, which is an open problem proposed by S. Trimberger (1998) for time-multiplexed FPGA partitioning. Time-multiplexed FPGAs have the potential to dramatically improve logic density by time-sharing logic. Schedule compression is an important step in partitioning for time-multiplexed FPGAs and can greatly influence the quality of the partitioning solution. We exactly solve the schedule compression problem by converting it to a constrained min-max path problem. We further extend our algorithm to minimize the communication cost during schedule compression. Experiments show that our optimal algorithm outperforms the existing heuristics and runs very efficiently.","PeriodicalId":6414,"journal":{"name":"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)","volume":"25 1","pages":"400-405"},"PeriodicalIF":0.0000,"publicationDate":"1999-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A graph theoretic optimal algorithm for schedule compression in time-multiplexed FPGA partitioning\",\"authors\":\"Huiqun Liu, D. F. Wong\",\"doi\":\"10.1109/ICCAD.1999.810683\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Presents an optimal algorithm to solve the schedule compression problem, which is an open problem proposed by S. Trimberger (1998) for time-multiplexed FPGA partitioning. Time-multiplexed FPGAs have the potential to dramatically improve logic density by time-sharing logic. Schedule compression is an important step in partitioning for time-multiplexed FPGAs and can greatly influence the quality of the partitioning solution. We exactly solve the schedule compression problem by converting it to a constrained min-max path problem. We further extend our algorithm to minimize the communication cost during schedule compression. Experiments show that our optimal algorithm outperforms the existing heuristics and runs very efficiently.\",\"PeriodicalId\":6414,\"journal\":{\"name\":\"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)\",\"volume\":\"25 1\",\"pages\":\"400-405\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAD.1999.810683\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD.1999.810683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A graph theoretic optimal algorithm for schedule compression in time-multiplexed FPGA partitioning
Presents an optimal algorithm to solve the schedule compression problem, which is an open problem proposed by S. Trimberger (1998) for time-multiplexed FPGA partitioning. Time-multiplexed FPGAs have the potential to dramatically improve logic density by time-sharing logic. Schedule compression is an important step in partitioning for time-multiplexed FPGAs and can greatly influence the quality of the partitioning solution. We exactly solve the schedule compression problem by converting it to a constrained min-max path problem. We further extend our algorithm to minimize the communication cost during schedule compression. Experiments show that our optimal algorithm outperforms the existing heuristics and runs very efficiently.