{"title":"Hardware-software codesign of multidimensional programs","authors":"Wayne Luk, Teddy Wu, Ian Page","doi":"10.1109/FPGA.1994.315604","DOIUrl":null,"url":null,"abstract":"Presents a method for parametrised partitioning of multidimensional programs for acceleration using a hardware coprocessor. The method involves a divide-and-conquer structure, with the \"divide\" and \"merge\" phases carried out by a general-purpose processor, while the \"conquer\" phase is handled by application-specific hardware. The partitioning strategy has been captured in a simple functional language, and we have automated the production of partitioned programs in this language. Our approach has been tested on an FPGA-based system using a number of computer vision algorithms, including the Canny edge detector, and the performance is compared against executing the programs on the PC host.<<ETX>>","PeriodicalId":138179,"journal":{"name":"Proceedings of IEEE Workshop on FPGA's for Custom Computing Machines","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE Workshop on FPGA's for Custom Computing Machines","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPGA.1994.315604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32
Abstract
Presents a method for parametrised partitioning of multidimensional programs for acceleration using a hardware coprocessor. The method involves a divide-and-conquer structure, with the "divide" and "merge" phases carried out by a general-purpose processor, while the "conquer" phase is handled by application-specific hardware. The partitioning strategy has been captured in a simple functional language, and we have automated the production of partitioned programs in this language. Our approach has been tested on an FPGA-based system using a number of computer vision algorithms, including the Canny edge detector, and the performance is compared against executing the programs on the PC host.<>
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