{"title":"Tabu search with intensification strategy for functional partitioning in hardware-software codesign","authors":"T. Wiangtong, P. Cheung, W. Luk","doi":"10.1109/FPGA.2002.1106691","DOIUrl":null,"url":null,"abstract":"This paper presents tabu search (TS) method with intensification strategy for hardware-software partitioning. The algorithm operates on functional blocks for designs represented as directed acyclic graphs (DAG), with the objective of minimising processing time under various hardware area constraints. Results are compared to two other heuristic search algorithms: genetic algorithm (GA) and simulated annealing (SA). The comparison involves a scheduling model based on list scheduling for calculating processing time used as a system cost, assuming that shared resource conflicts do not occur. The results show that TS, which rarely appears for solving this kind of problem, is superior to SA and GA in terms of both search time and the quality of solutions. In addition, we have implemented intensification strategy in TS called penalty reward, which can further improve the quality of results.","PeriodicalId":272235,"journal":{"name":"Proceedings. 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPGA.2002.1106691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
This paper presents tabu search (TS) method with intensification strategy for hardware-software partitioning. The algorithm operates on functional blocks for designs represented as directed acyclic graphs (DAG), with the objective of minimising processing time under various hardware area constraints. Results are compared to two other heuristic search algorithms: genetic algorithm (GA) and simulated annealing (SA). The comparison involves a scheduling model based on list scheduling for calculating processing time used as a system cost, assuming that shared resource conflicts do not occur. The results show that TS, which rarely appears for solving this kind of problem, is superior to SA and GA in terms of both search time and the quality of solutions. In addition, we have implemented intensification strategy in TS called penalty reward, which can further improve the quality of results.