{"title":"基于FPGA的粒子群算法","authors":"Monia Ettouil, Habib Smei, A. Jemai","doi":"10.1109/ICM.2018.8704047","DOIUrl":null,"url":null,"abstract":"The particle swarm optimization PSO is an attractive domain for community looking to enhance time for optimal solutions. Several works will be done for SW or SW/HW implementation. The latter often gives better performance. In this paper, we present a comparative study of these various solutions and we focus on the FPGA ones. Our approach is based on a codesign methodology which adjusts performance parameters at design time.","PeriodicalId":305356,"journal":{"name":"2018 30th International Conference on Microelectronics (ICM)","volume":"ED-13 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Particle Swarm Optimization on FPGA\",\"authors\":\"Monia Ettouil, Habib Smei, A. Jemai\",\"doi\":\"10.1109/ICM.2018.8704047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The particle swarm optimization PSO is an attractive domain for community looking to enhance time for optimal solutions. Several works will be done for SW or SW/HW implementation. The latter often gives better performance. In this paper, we present a comparative study of these various solutions and we focus on the FPGA ones. Our approach is based on a codesign methodology which adjusts performance parameters at design time.\",\"PeriodicalId\":305356,\"journal\":{\"name\":\"2018 30th International Conference on Microelectronics (ICM)\",\"volume\":\"ED-13 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 30th International Conference on Microelectronics (ICM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICM.2018.8704047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 30th International Conference on Microelectronics (ICM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICM.2018.8704047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The particle swarm optimization PSO is an attractive domain for community looking to enhance time for optimal solutions. Several works will be done for SW or SW/HW implementation. The latter often gives better performance. In this paper, we present a comparative study of these various solutions and we focus on the FPGA ones. Our approach is based on a codesign methodology which adjusts performance parameters at design time.
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