{"title":"Multi-Objective Genetic optimized multiprocessor SoC design","authors":"M. Arjomand, H. Sarbazi-Azad, S. Amiri","doi":"10.1109/ISSOC.2008.4694887","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a new multi-objective genetic algorithm (MOGA) for mapping a given set of intellectual property onto a network-on-chip architecture such that for a specific application total communication cost and energy consumption become optimized while bandwidth constraints are satisfied. As the main theoretical contribution, we first introduce a generic queuing model to estimate performance and an experimental energy consumption model during the design phase, with acceptable accuracy. Then, an efficient genetic algorithm employs these models to propose a Pareto optimal front for an application and an arbitrary topology. Experimental results show that the proposed algorithm is very fast which results in a new approach for mapping MPSoC cores on chip.","PeriodicalId":168022,"journal":{"name":"2008 International Symposium on System-on-Chip","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on System-on-Chip","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSOC.2008.4694887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we introduce a new multi-objective genetic algorithm (MOGA) for mapping a given set of intellectual property onto a network-on-chip architecture such that for a specific application total communication cost and energy consumption become optimized while bandwidth constraints are satisfied. As the main theoretical contribution, we first introduce a generic queuing model to estimate performance and an experimental energy consumption model during the design phase, with acceptable accuracy. Then, an efficient genetic algorithm employs these models to propose a Pareto optimal front for an application and an arbitrary topology. Experimental results show that the proposed algorithm is very fast which results in a new approach for mapping MPSoC cores on chip.