时间累积概率密度函数的有效计算

Yu-Shih Su, Y. Weng, Shih-Chieh Chang
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引用次数: 3

摘要

众所周知,设计的关键路径很少被激活,因为长路径的敏化需要在适当的时间对许多门施加适当的值。一些研究使用稀有激活的概念进行性能优化,但它们需要稀有激活的准确概率信息。我们将时间累积概率密度函数(time - cdf)定义为由输入模式引起的电路延迟的概率分布。由于time - cdf在许多应用中非常有用,因此本文提出了一种构造time - cdf的有效方法。我们的基本思想利用了一个称为时间特征函数的概念,它允许我们执行非常快速的并行仿真,而不是传统的事件驱动仿真。平均而言,我们的结果比传统模拟快6.18倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Calculation of Timed Cumulative Probability Density Function
It is well known that critical paths of a design are rarely activated because sensitization of long paths requires appropriate values applied at appropriate time for many gates. Several researches use the concept of rare activation for performance optimization but they require the accurate probability information of rare activation. We define timed cumulative probability density function (Timed-CDF) to be the probability distribution of a circuit's delay induced by input patterns. Because a Timed-CDF is very useful for many applications, in this paper, we present an efficient way for constructing a Timed-CDF. Our basic idea utilizes a concept called timed characteristic function, which allows us to perform very fast parallel simulation rather than the traditional event-driven simulation. On average, our results are 6.18 times faster then the traditional simulation.
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