S. Leventhal, Lin Yuan, N. Bambha, S. Bhattacharyya, G. Qu
{"title":"基于进化算法的DSP地址优化","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":"{\"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}","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}
DSP address optimization using evolutionary algorithms
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.