NDLI: Stochastic variational analysis of large power grids considering intra-die correlations

Content Provider	IEEE Xplore Digital Library
Author	Ghanta, P. Vrudhula, S. Bhardwaj, S. Panda, R.
Copyright Year	2006
Description	Author affiliation: Dept. of EE, Arizona State Univ., Tempe, AZ (Ghanta, P.)
Abstract	For statistical timing and power analysis that are very important problems in the sub-100 nm technologies, stochastic analysis of power grids that characterizes the voltage fluctuations due to process variations is inevitable. In this paper, we propose an efficient algorithm for the variational analysis of large power grids in the presence of a significant number of Gaussian intra-die process variables that are correlated. We consider variations in the power grid's electrical parameters as spatial stochastic processes and express them as linear expansions in an orthonormal series of random variables using the Karhunen-Loeve (KLE) method. The voltage response is then represented as an orthonormal polynomial series and the coefficients are obtained optimally using the Galerkin method. We propose a novel method to separate the stochastic analysis for the random variables that effect only the inputs (e.g, drain currents) and for those that effect the system parameters as well (e.g., conductance, capacitance). We show that this parallelism can result in significant speed-ups in addition to the speed-ups inherent to Galerkin-based methods. Our analysis has been applied to several industrial power grids and the results show speed-ups of up to two orders of magnitude over Monte Carlo simulations for comparable accuracy
Sponsorship	SiCda EDA Consortium IEEE Circuits & Syst. Soc. IEEE CASS/CANDE CANDE CEDA
Starting Page	211
Ending Page	216
File Size	1967884
Page Count	6
File Format	PDF
ISBN	1595933816
ISSN	0738100X
DOI	10.1109/DAC.2006.229196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Stochastic processes Power grids Random variables Moment methods Timing Voltage fluctuations Algorithm design and analysis Polynomials Stochastic systems Capacitance Orthonormal Polynomials Algorithms Performance Verification Power Grids Process Variations Correlations Stochastic Analysis Polynomial Chaos
Content Type	Text
Resource Type	Article

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