Oussama Djedidi, M. Djeziri, N. M'Sirdi, A. Naamane
{"title":"Constructing an Accurate and a High-Performance Power Profiler for Embedded Systems and Smartphones","authors":"Oussama Djedidi, M. Djeziri, N. M'Sirdi, A. Naamane","doi":"10.1145/3242102.3242139","DOIUrl":null,"url":null,"abstract":"The main objective of this paper is to present a new accurate power profiler for embedded systems and smartphones. The second objective is, for it, to be a tutorial explaining the main steps to build power profilers for embedded and mobile systems, in general. We start our work by firstly describing the general methodology of building a power profiler. Then, we showcase how each step is undertaken to build a profiler with two power models. The first one was an artificial neural network (called N2) that presented a lot of noise in its estimation. After debugging and improvement, the second model, a NARX neural network (we call N3) was built. It eliminated all the drawback of the first model and had a mean absolute percentage error of 2.8%.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"24 51","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3242102.3242139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The main objective of this paper is to present a new accurate power profiler for embedded systems and smartphones. The second objective is, for it, to be a tutorial explaining the main steps to build power profilers for embedded and mobile systems, in general. We start our work by firstly describing the general methodology of building a power profiler. Then, we showcase how each step is undertaken to build a profiler with two power models. The first one was an artificial neural network (called N2) that presented a lot of noise in its estimation. After debugging and improvement, the second model, a NARX neural network (we call N3) was built. It eliminated all the drawback of the first model and had a mean absolute percentage error of 2.8%.