{"title":"Towards code generation from design models for embedded systems on heterogeneous CPU-GPU platforms","authors":"Federico Ciccozzi","doi":"10.1109/ETFA.2013.6648139","DOIUrl":null,"url":null,"abstract":"The complexity of modern embedded systems is ever increasing and the selection of target platforms is shifting from homogeneous to more heterogeneous and powerful configurations. In our previous works, we exploited the power of model-driven techniques to deal with such complexity by enabling the automatic generation of full-fledged functional code from UML models enriched with ALF action code. Nevertheless, the scope was bounded to CPU-based platforms. In this work we propose a preliminary definition of the means to build upon the current code generator to enable the generation of code targeting heterogeneous platforms, more specifically conceiving mixed CPU-GPU configurations. The aim is to minimise the effort of the user in modelling platform-related information by embedding the greatest feasible amount of it into the transformation process.","PeriodicalId":106678,"journal":{"name":"2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2013.6648139","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
The complexity of modern embedded systems is ever increasing and the selection of target platforms is shifting from homogeneous to more heterogeneous and powerful configurations. In our previous works, we exploited the power of model-driven techniques to deal with such complexity by enabling the automatic generation of full-fledged functional code from UML models enriched with ALF action code. Nevertheless, the scope was bounded to CPU-based platforms. In this work we propose a preliminary definition of the means to build upon the current code generator to enable the generation of code targeting heterogeneous platforms, more specifically conceiving mixed CPU-GPU configurations. The aim is to minimise the effort of the user in modelling platform-related information by embedding the greatest feasible amount of it into the transformation process.