{"title":"A novel parallel adaptation of an implicit path delay grading method","authors":"Joseph Lenox, S. Tragoudas","doi":"10.1145/2591513.2591539","DOIUrl":null,"url":null,"abstract":"For large modern circuits, it is desirable to trade hardware cost for time when making path delay fault coverage estimates, especially as a subroutine for ATPG and timing analysis solutions. A parallel adaptation of an established framework for implicit path delay fault grading on with a GPGPU implementation is presented. Experimental evaluation on a NVIDIA Tesla C2075 GPU shows on average 50x speedup against the basic version for the framework on an Intel Xeon E5504 host system. Over a 1200x speedup is observed against a single-threaded, more complex version in the framework which grades more faults.","PeriodicalId":272619,"journal":{"name":"ACM Great Lakes Symposium on VLSI","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2591513.2591539","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
For large modern circuits, it is desirable to trade hardware cost for time when making path delay fault coverage estimates, especially as a subroutine for ATPG and timing analysis solutions. A parallel adaptation of an established framework for implicit path delay fault grading on with a GPGPU implementation is presented. Experimental evaluation on a NVIDIA Tesla C2075 GPU shows on average 50x speedup against the basic version for the framework on an Intel Xeon E5504 host system. Over a 1200x speedup is observed against a single-threaded, more complex version in the framework which grades more faults.
对于大型现代电路,在进行路径延迟故障覆盖估计时,特别是作为ATPG和时序分析解决方案的子程序,需要用硬件成本换取时间。提出了一种基于GPGPU实现的隐式路径延迟故障分级框架的并行适应方法。在NVIDIA Tesla C2075 GPU上的实验评估显示,与英特尔至强E5504主机系统上的框架基本版本相比,该框架的平均加速速度提高了50倍。对于单线程、更复杂的框架版本,可以观察到超过1200倍的加速,该版本可以对更多的错误进行分级。