Falk Wurst, D. Dasari, A. Hamann, D. Ziegenbein, Ignacio Sañudo, Nicola Capodieci, M. Bertogna, P. Burgio
{"title":"System Performance Modelling of Heterogeneous HW Platforms: An Automated Driving Case Study","authors":"Falk Wurst, D. Dasari, A. Hamann, D. Ziegenbein, Ignacio Sañudo, Nicola Capodieci, M. Bertogna, P. Burgio","doi":"10.1109/DSD.2019.00060","DOIUrl":null,"url":null,"abstract":"The push towards automated and connected driving functionalities mandates the use of heterogeneous HW platforms in order to provide the required computational resources. For these platforms, the established methods for performance modelling in industry are no longer effective. In this paper, we propose an initial modelling concept for heterogeneous platforms which can then be fed into appropriate tools to derive effective performance predictions. The approach is demonstrated for a prototypical automated driving application on the Nvidia Tegra X2 platform.","PeriodicalId":217233,"journal":{"name":"2019 22nd Euromicro Conference on Digital System Design (DSD)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 22nd Euromicro Conference on Digital System Design (DSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSD.2019.00060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
The push towards automated and connected driving functionalities mandates the use of heterogeneous HW platforms in order to provide the required computational resources. For these platforms, the established methods for performance modelling in industry are no longer effective. In this paper, we propose an initial modelling concept for heterogeneous platforms which can then be fed into appropriate tools to derive effective performance predictions. The approach is demonstrated for a prototypical automated driving application on the Nvidia Tegra X2 platform.