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Stochastic Extended Krylov Subspace Method for Variational Analysis of On-Chip Power Grid Networks

机译:随机扩展Krylov子空间方法,用于片上电网网络变分析

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In this paper, we propose a novel stochastic method for analyzing the voltage drop variations of on-chip power grid networks with log-normal leakage current variations. The new method, called StoEKS, applies Hermite polynomial chaos (PC) to represent the random variables in both power grid networks and input leakage currents. But different from the existing Hermit PC based stochastic simulation method, extended Krylov subspace method (EKS) is employed to compute variational responses using the augmented matrices consisting of the coefficients of Hermite polynomials. Our contribution lies in the combination of the statistical spectrum method with the extended Krylov subspace method to fast solve the variational circuit equations for the first time. Experimental results show that the proposed method is about two-order magnitude faster than the existing Hermite PC based simulation method and more order of magnitudes faster than Monte Carlo methods with marginal errors. StoEKS also can analyze much larger circuits than the exiting Hermit PC based methods.
机译:在本文中,我们提出了一种新的随机方法,用于分析芯片电网网络的电压降差,具有对数正常漏电流变化。新方法称为软件,适用Hermite多项式混沌(PC)来表示电网网络和输入漏电流中的随机变量。但是与现有的隐士基于PC的随机仿真方法不同,采用扩展Krylov子空间方法(EKS)来使用由Hermite多项式的系数组成的增强矩阵来计算变分响应。我们的贡献在于统计频谱方法与扩展Krylov子空间方法的组合,首次快速解决变分电路方程。实验结果表明,该方法比现有的Hermite PC基于PC的仿真方法更快,比蒙特卡罗方法更快,比具有边缘误差的Monte Carlo方法更快。 STOEKS还可以分析比退出隐士基于PC的方法更大的电路。

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