{"title":"Accelerating source-level timing simulation","authors":"Simon Schulz, O. Bringmann","doi":"10.3850/9783981537079_0234","DOIUrl":null,"url":null,"abstract":"Source-level timing simulation (SLTS) is a promising method to overcome one major challenge in early and rapid prototyping: fast and accurate simulation of timing behavior. However, most of existing SLTS approaches are still coupled with a considerable simulation overhead. We present a method to reduce source-level timing simulation overhead by removing superfluous instrumentation based on instrumentation dependency graphs. We show in experiments, that our optimizations decrease simulation overhead significantly (up to factor 7.7), without losing accuracy. Our detailed experiments are based on benchmarks as well as real life production code, that is simulated in a virtual environment.","PeriodicalId":311352,"journal":{"name":"2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3850/9783981537079_0234","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
Source-level timing simulation (SLTS) is a promising method to overcome one major challenge in early and rapid prototyping: fast and accurate simulation of timing behavior. However, most of existing SLTS approaches are still coupled with a considerable simulation overhead. We present a method to reduce source-level timing simulation overhead by removing superfluous instrumentation based on instrumentation dependency graphs. We show in experiments, that our optimizations decrease simulation overhead significantly (up to factor 7.7), without losing accuracy. Our detailed experiments are based on benchmarks as well as real life production code, that is simulated in a virtual environment.