Faizal Arya Samman, S. Pongyupinpanich, M. Glesner
{"title":"Reconfigurable streaming processor core with interconnected floating-point arithmetic units for multicore adaptive signal processing systems","authors":"Faizal Arya Samman, S. Pongyupinpanich, M. Glesner","doi":"10.1109/ReCoSoC.2011.5981539","DOIUrl":null,"url":null,"abstract":"A reconfigurable and programmable streaming processor core complemented with interconnected arithmetic units for the acceleration of floating-point operations is presented in this paper. The streaming processor can be easily reconfigured to perform a complex scientific algorithm or computations by changing the set of instructions in a central control unit. By using floating-point arithmetic unit with pipeline streaming data flow, floating-point operations can be performed in each cycle resulting in a high-performance scientific computations. The streaming processor is dedicated for a high-performance adaptive signal processing applications. For higher performance, reliability and fault-tolerance scientific computations, the streaming processor would be designed as a tile processor in a multicore streaming processor system.","PeriodicalId":103130,"journal":{"name":"6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ReCoSoC.2011.5981539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
A reconfigurable and programmable streaming processor core complemented with interconnected arithmetic units for the acceleration of floating-point operations is presented in this paper. The streaming processor can be easily reconfigured to perform a complex scientific algorithm or computations by changing the set of instructions in a central control unit. By using floating-point arithmetic unit with pipeline streaming data flow, floating-point operations can be performed in each cycle resulting in a high-performance scientific computations. The streaming processor is dedicated for a high-performance adaptive signal processing applications. For higher performance, reliability and fault-tolerance scientific computations, the streaming processor would be designed as a tile processor in a multicore streaming processor system.