Stochastic dominant singular vectors method for variation-aware extraction
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Daniel-Stochastic Dominant.pdf
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Author(s)
El-Moselhy, Tarek Ali
Daniel, Luca
Date Issued
January 2010
Journal
Proceedings of the 47th Design Automation Conference (DAC '10 )
Publisher
Association for Computing Machinery (ACM)
Citation
Tarek El-Moselhy and Luca Daniel. 2010. Stochastic dominant singular vectors method for variation-aware extraction. In Proceedings of the 47th Design Automation Conference (DAC '10). ACM, New York, NY, USA, 667-672. Copyright © 2010 ACM, Inc.
Version
Final published version
Abstract
In this paper we present an efficient algorithm for variation-aware interconnect extraction. The problem we are addressing can be formulated mathematically as the solution of linear systems with matrix coefficients that are dependent on a set of random variables. Our algorithm is based on representing the solution vector as a summation of terms. Each term is a product of an unknown vector in the deterministic space and an unknown direction in the stochastic space. We then formulate a simple nonlinear optimization problem which uncovers sequentially the most relevant directions in the combined deterministic-stochastic space. The complexity of our algorithm scales with the sum (rather than the product) of the sizes of the deterministic and stochastic spaces, hence it is orders of magnitude more efficient than many of the available state of the art techniques. Finally, we validate our algorithm on a variety of onchip and off-chip capacitance and inductance extraction problems, ranging from moderate to very large size, not feasible using any of the available state of the art techniques.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/1837274.1837444
