{"title":"用于优化转换的集成框架","authors":"Shan-Hsi Huang, J. Rabaey","doi":"10.1109/VLSISP.1996.558359","DOIUrl":null,"url":null,"abstract":"This paper proposes a framework aimed at the optimization of speed, area, or power consumption of custom ASIC DSP designs through algorithmic transformations. This framework systematically selects and orders transformations for optimization. The methodology behind the framework combines bottleneck analysis (why the transformations should be applied), transformation ordering (the order in which the transformations are applied), algorithm partitioning (which parts of an algorithm should be transformed), transformation analysis/selection (which transformations to apply), and transformation execution (how to apply the selected transformations). Assisted by this framework, designers can easily and quickly exploit a variety of optimizing transformations to explore the algorithmic design space to reach better designs.","PeriodicalId":290885,"journal":{"name":"VLSI Signal Processing, IX","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An integrated framework for optimizing transformations\",\"authors\":\"Shan-Hsi Huang, J. Rabaey\",\"doi\":\"10.1109/VLSISP.1996.558359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a framework aimed at the optimization of speed, area, or power consumption of custom ASIC DSP designs through algorithmic transformations. This framework systematically selects and orders transformations for optimization. The methodology behind the framework combines bottleneck analysis (why the transformations should be applied), transformation ordering (the order in which the transformations are applied), algorithm partitioning (which parts of an algorithm should be transformed), transformation analysis/selection (which transformations to apply), and transformation execution (how to apply the selected transformations). Assisted by this framework, designers can easily and quickly exploit a variety of optimizing transformations to explore the algorithmic design space to reach better designs.\",\"PeriodicalId\":290885,\"journal\":{\"name\":\"VLSI Signal Processing, IX\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VLSI Signal Processing, IX\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSISP.1996.558359\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VLSI Signal Processing, IX","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSISP.1996.558359","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An integrated framework for optimizing transformations
This paper proposes a framework aimed at the optimization of speed, area, or power consumption of custom ASIC DSP designs through algorithmic transformations. This framework systematically selects and orders transformations for optimization. The methodology behind the framework combines bottleneck analysis (why the transformations should be applied), transformation ordering (the order in which the transformations are applied), algorithm partitioning (which parts of an algorithm should be transformed), transformation analysis/selection (which transformations to apply), and transformation execution (how to apply the selected transformations). Assisted by this framework, designers can easily and quickly exploit a variety of optimizing transformations to explore the algorithmic design space to reach better designs.