一种新的并行自适应隐式路径延迟分级方法

Joseph Lenox, S. Tragoudas
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引用次数: 1

摘要

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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel parallel adaptation of an implicit path delay grading method
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.
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