S. Leventhal, Lin Yuan, N. Bambha, S. Bhattacharyya, G. Qu
{"title":"DSP address optimization using evolutionary algorithms","authors":"S. Leventhal, Lin Yuan, N. Bambha, S. Bhattacharyya, G. Qu","doi":"10.1145/1140389.1140399","DOIUrl":null,"url":null,"abstract":"Offset assignment has been studied as a highly effective approach to code optimization in modern digital signal processors (DSPs). In this paper, we propose two evolutionary algorithms to solve the general offset assignment problem with k address registers and an arbitrary auto-modify range. These algorithms differ from previous algorithms by having the capability of visiting the entire search space. We implement and analyze a variety of existing general offset assignment algorithms and test them on a set of standard benchmarks. The algorithms we propose can achieve a performance improvement of up to 31% over the best existing algorithm. We also achieve an average of 14% improvement over the union of recently proposed algorithms.","PeriodicalId":375451,"journal":{"name":"Software and Compilers for Embedded Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software and Compilers for Embedded Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1140389.1140399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Offset assignment has been studied as a highly effective approach to code optimization in modern digital signal processors (DSPs). In this paper, we propose two evolutionary algorithms to solve the general offset assignment problem with k address registers and an arbitrary auto-modify range. These algorithms differ from previous algorithms by having the capability of visiting the entire search space. We implement and analyze a variety of existing general offset assignment algorithms and test them on a set of standard benchmarks. The algorithms we propose can achieve a performance improvement of up to 31% over the best existing algorithm. We also achieve an average of 14% improvement over the union of recently proposed algorithms.