{"title":"A Genetic Algorithm Based Solution for Dynamically Reconfigurable Modules Allocation","authors":"V. Rana, C. Sandionigi, M. Santambrogio","doi":"10.1109/SPL.2007.371745","DOIUrl":null,"url":null,"abstract":"The advances in the programmable hardware have lead to new architectures, where the hardware can be dynamically adapted to the application to gain better performance. One of the problems in realizing dynamically reconfigurable systems is the allocation of dynamically reconfigurable modules. In this scenario, when a new module has to be reconfigured in the system, there is the need to find a suitable free place where it can be configured. In this work a genetic algorithm has been developed to solve the problem of dynamically reconfigurable modules allocation. The search task has been modeled with a genetic algorithm in which each chromosome represents a configuration status of the programmable devices and both crossover and mutation processes try to change the previously found location for the new module in order to achieve a better fitness, that stands for the goodness of the final solution.","PeriodicalId":419253,"journal":{"name":"2007 3rd Southern Conference on Programmable Logic","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 3rd Southern Conference on Programmable Logic","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPL.2007.371745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The advances in the programmable hardware have lead to new architectures, where the hardware can be dynamically adapted to the application to gain better performance. One of the problems in realizing dynamically reconfigurable systems is the allocation of dynamically reconfigurable modules. In this scenario, when a new module has to be reconfigured in the system, there is the need to find a suitable free place where it can be configured. In this work a genetic algorithm has been developed to solve the problem of dynamically reconfigurable modules allocation. The search task has been modeled with a genetic algorithm in which each chromosome represents a configuration status of the programmable devices and both crossover and mutation processes try to change the previously found location for the new module in order to achieve a better fitness, that stands for the goodness of the final solution.