{"title":"Hardware support for performance measurements and energy estimation of OpenRISC processor","authors":"Lucian Bara, O. Boncalo, M. Marcu","doi":"10.1109/SACI.2015.7208237","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of providing support for energy consumption accounting and performance evaluation by means of performance counters in an open source processor core - OpenRISC OR1200. The OpenRISC processing core is flexible in that it allows different hardware configurations, and provides full support on the tool-chain side. In addition to this, it gives full hardware design access, and it is used by a well-established community. This paper has taken advantage of these features in order to study how different processing core's architecture configurations and compiler parameters influence the processing core's performance. Furthermore, an energy consumption model based on performance counters values correlated by physical measurements has been proposed.","PeriodicalId":312683,"journal":{"name":"2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI.2015.7208237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper addresses the problem of providing support for energy consumption accounting and performance evaluation by means of performance counters in an open source processor core - OpenRISC OR1200. The OpenRISC processing core is flexible in that it allows different hardware configurations, and provides full support on the tool-chain side. In addition to this, it gives full hardware design access, and it is used by a well-established community. This paper has taken advantage of these features in order to study how different processing core's architecture configurations and compiler parameters influence the processing core's performance. Furthermore, an energy consumption model based on performance counters values correlated by physical measurements has been proposed.