{"title":"Context-Aware Dataflow Adaptation Technique for Low-Power Multi-Core Embedded Systems","authors":"Hyeonseok Jung, Hoeseok Yang","doi":"10.1145/3195970.3196015","DOIUrl":null,"url":null,"abstract":"Today’s embedded systems operate under increasingly dynamic conditions. First, computational workloads can be either fluctuating or adjustable. Moreover, as many devices are battery-powered, it is common to have runtime power management technique, which results in dynamic power budget. This paper presents a design methodology for multi-core systems, based on dataflow specification, that can deal with various contexts. We optimize the original dataflow considering various working conditions, then, autonomously adapt it to a pre-defined optimal form in response to context changes. We show the effectiveness of the proposed technique with a real-life case study and synthetic benchmarks.","PeriodicalId":6491,"journal":{"name":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3195970.3196015","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
Today’s embedded systems operate under increasingly dynamic conditions. First, computational workloads can be either fluctuating or adjustable. Moreover, as many devices are battery-powered, it is common to have runtime power management technique, which results in dynamic power budget. This paper presents a design methodology for multi-core systems, based on dataflow specification, that can deal with various contexts. We optimize the original dataflow considering various working conditions, then, autonomously adapt it to a pre-defined optimal form in response to context changes. We show the effectiveness of the proposed technique with a real-life case study and synthetic benchmarks.