基于统计预测引擎的电源噪声感知动态时序分析方法

M. Tsiampas, N. Evmorfopoulos, Konstantis Daloukas, J. Moondanos, G. Stamoulis
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引用次数: 1

摘要

随着技术的不断萎缩,工业界寻求更快的超低功耗集成电路,这需要更准确地估计最坏情况下的延迟。尽管传统的静态时序分析(STA)方法纳入了有关供电网络互连和噪声的数据,但它们仍然被认为过于悲观。在估计最坏情况延迟时,准确捕捉动态影响的唯一方法是通过动态时序分析(DTA)。本文提出了一种新的方法,利用极值理论对电压降感知仿真结果精确估计最坏情况延迟的紧上界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A power-supply noise aware dynamic timing analysis methodology, based on a statistical prediction engine
As technologies continue to shrink, industry seeks even faster ultra-low power ICs, requiring more accurate estimation of the worst case delay. Although traditional Static Timing Analysis (STA) methods incorporate data regarding interconnects and noise over power supply networks, they are still considered to be overly pessimistic. The only way to accurately capture dynamic effects in the estimation of the worst case delay is through Dynamic Timing Analysis (DTA). In this paper we propose a novel methodology to precisely estimate a tight upper bound of the worst case delay, using Extreme Value Theory on the results of voltage drop-aware simulation.
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