{"title":"基于遗传规划的基于条件任务图的分布式嵌入式系统软硬件协同算法","authors":"Adam Górski, M. Ogorzałek","doi":"10.5220/0011011700003118","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. Unlike other genetic programming solutions for distributed embedded systems in this work the system is specified using conditional task graph. In such a graph every node represents a single task. The edge represents amount of data needed to be transferred between connected tasks, however some of the edges can be conditional. The data is transferred using those edges only if condition is satisfied. Proposed methodology is based on genetic programming. Therefore the genotype is a system construction tree. In each nodes of the tree are system building options. The next generations are obtained using standard genetic operators: mutation, crossover, cloning and selection.","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"158 1","pages":"239-243"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genetic Programming based Algorithm for HW/SW Cosynthesis of Distributed Embedded Systems Specified using Conditional Task Graph\",\"authors\":\"Adam Górski, M. Ogorzałek\",\"doi\":\"10.5220/0011011700003118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. Unlike other genetic programming solutions for distributed embedded systems in this work the system is specified using conditional task graph. In such a graph every node represents a single task. The edge represents amount of data needed to be transferred between connected tasks, however some of the edges can be conditional. The data is transferred using those edges only if condition is satisfied. Proposed methodology is based on genetic programming. Therefore the genotype is a system construction tree. In each nodes of the tree are system building options. The next generations are obtained using standard genetic operators: mutation, crossover, cloning and selection.\",\"PeriodicalId\":72028,\"journal\":{\"name\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"volume\":\"158 1\",\"pages\":\"239-243\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0011011700003118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0011011700003118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic Programming based Algorithm for HW/SW Cosynthesis of Distributed Embedded Systems Specified using Conditional Task Graph
In this paper we propose a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. Unlike other genetic programming solutions for distributed embedded systems in this work the system is specified using conditional task graph. In such a graph every node represents a single task. The edge represents amount of data needed to be transferred between connected tasks, however some of the edges can be conditional. The data is transferred using those edges only if condition is satisfied. Proposed methodology is based on genetic programming. Therefore the genotype is a system construction tree. In each nodes of the tree are system building options. The next generations are obtained using standard genetic operators: mutation, crossover, cloning and selection.