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dc.contributor.authorEl-Moselhy, Tarek Ali
dc.contributor.authorDaniel, Luca
dc.date.accessioned2012-08-17T20:33:58Z
dc.date.available2012-08-17T20:33:58Z
dc.date.issued2010-01
dc.identifier.isbn978-1-4503-0002-5
dc.identifier.urihttp://hdl.handle.net/1721.1/72204
dc.description.abstractIn 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.en_US
dc.description.sponsorshipFocus Center Research Program. Focus Center for Circuit & System Solutions (C2S2) (Interconnect Focus Center)en_US
dc.description.sponsorshipMentor Graphics (Firm)en_US
dc.description.sponsorshipInternational Business Machines Corporationen_US
dc.description.sponsorshipAdvanced Micro Devices (Firm)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/1837274.1837444en_US
dc.rightsArticle 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.en_US
dc.sourceIEEEen_US
dc.titleStochastic dominant singular vectors method for variation-aware extractionen_US
dc.typeArticleen_US
dc.identifier.citationTarek 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.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverDaniel, Luca
dc.contributor.mitauthorEl-Moselhy, Tarek Ali
dc.contributor.mitauthorDaniel, Luca
dc.relation.journalProceedings of the 47th Design Automation Conference (DAC '10 )en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsEl-Moselhy, Tarek; Daniel, Lucaen
dc.identifier.orcidhttps://orcid.org/0000-0002-5880-3151
mit.licensePUBLISHER_POLICYen_US


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