{"title":"Domain-Adaptive Soft Real-Time Hybrid Application Mapping for MPSoCs","authors":"J. Spieck, S. Wildermann, Jürgen Teich","doi":"10.1109/MLCAD52597.2021.9531269","DOIUrl":null,"url":null,"abstract":"The mapping of soft real-time applications onto heterogeneous MPSoC architectures can have a high influence on execution properties like energy consumption or the number of deadline violations. In recent years, scenario-aware hybrid application mapping (HAM) has turned out as the state-of-the-art mapping method for input-dependent applications whose execution characteristics are in strong dependence on the input that shall be processed. In this work, we propose an extension of scenario-aware HAM that is capable of transferring its mapping strategy learned from a labeled source data domain using supervised learning into an unlabeled target domain that exhibits a shift in its data distribution. Our domain-adaptive HAM employs a run-time manager (RTM) that performs mapping selection and reconfiguration at run time based on general domain-invariant knowledge learned at design time that is valid for both the source and target domain. Evaluation based on two input-dependent applications and two MPSoC architectures demonstrates that our domain-adaptive HAM consistently outperforms state-of-the-art mapping procedures with regard to the number of deadline misses and energy consumption in presence of a domain shift. Furthermore, our HAM approach obtains results close to an explicit optimization for the target domain in a fraction of the necessary optimization time and without necessitating target labels.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MLCAD52597.2021.9531269","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 mapping of soft real-time applications onto heterogeneous MPSoC architectures can have a high influence on execution properties like energy consumption or the number of deadline violations. In recent years, scenario-aware hybrid application mapping (HAM) has turned out as the state-of-the-art mapping method for input-dependent applications whose execution characteristics are in strong dependence on the input that shall be processed. In this work, we propose an extension of scenario-aware HAM that is capable of transferring its mapping strategy learned from a labeled source data domain using supervised learning into an unlabeled target domain that exhibits a shift in its data distribution. Our domain-adaptive HAM employs a run-time manager (RTM) that performs mapping selection and reconfiguration at run time based on general domain-invariant knowledge learned at design time that is valid for both the source and target domain. Evaluation based on two input-dependent applications and two MPSoC architectures demonstrates that our domain-adaptive HAM consistently outperforms state-of-the-art mapping procedures with regard to the number of deadline misses and energy consumption in presence of a domain shift. Furthermore, our HAM approach obtains results close to an explicit optimization for the target domain in a fraction of the necessary optimization time and without necessitating target labels.