Yi Lu, T. Marconi, G. Gaydadjiev, K. Bertels, R. Meeuws
{"title":"A self-adaptive on-line task placement algorithm for partially reconfigurable systems","authors":"Yi Lu, T. Marconi, G. Gaydadjiev, K. Bertels, R. Meeuws","doi":"10.1109/IPDPS.2008.4536505","DOIUrl":null,"url":null,"abstract":"With the arrival of partial reconfiguration technology, modern FPGAs support swapping tasks in or out individually at run-time without interrupting other tasks running on the same FPGA. Although, implementing this feature achieves much better flexibility and device utilization, the challenge remains to quickly and efficiently place tasks arriving at run-time on such partially reconfigurable systems. In this paper, we propose an algorithm to handle this on-line, run-time task placement problem. By adding logical constraints on the FPGA and introducing our resources management solution, the simulation results show our algorithm has better overall performances compared with previous reported methods in terms of task rejection number, placement quality and execution time.","PeriodicalId":162608,"journal":{"name":"2008 IEEE International Symposium on Parallel and Distributed Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Parallel and Distributed Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2008.4536505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
With the arrival of partial reconfiguration technology, modern FPGAs support swapping tasks in or out individually at run-time without interrupting other tasks running on the same FPGA. Although, implementing this feature achieves much better flexibility and device utilization, the challenge remains to quickly and efficiently place tasks arriving at run-time on such partially reconfigurable systems. In this paper, we propose an algorithm to handle this on-line, run-time task placement problem. By adding logical constraints on the FPGA and introducing our resources management solution, the simulation results show our algorithm has better overall performances compared with previous reported methods in terms of task rejection number, placement quality and execution time.