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A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

Published: 07 June 2004 Publication History

Abstract

We propose a novel, nonsimulative probabilistic model for switching activity in sequential circuits, capturing both spatio-temporal correlations at internal nodes and higher order temporal correlations due to feedback. This model, which we refer to as the temporal dependency model (TDM), can be constructed from the logic structure and is shown to be a dynamic Bayesian network. Dynamic Bayesian networks are extremely powerful in modeling high order temporal, as well as spatial, correlations; TDM is an exact model for the underlying conditional independencies. The attractive feature of this graphical representation of the joint probability function is not only that it makes the dependency relationships amongst nodes explicit, but it also serves as a computational mechanism for probabilistic inference. We report average errors in switching probability of 0.006, with errors tightly distributed around mean error values, on ISCAS'89 benchmark circuits involving up to 10000 signals.

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  • (2007)Probabilistic error modeling for sequential logic2007 7th IEEE Conference on Nanotechnology (IEEE NANO)10.1109/NANO.2007.4601266(616-620)Online publication date: Aug-2007
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  1. A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

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    cover image ACM Transactions on Design Automation of Electronic Systems
    ACM Transactions on Design Automation of Electronic Systems  Volume 11, Issue 3
    July 2006
    262 pages
    ISSN:1084-4309
    EISSN:1557-7309
    DOI:10.1145/1142980
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    Association for Computing Machinery

    New York, NY, United States

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    Published: 07 June 2004
    Published in TODAES Volume 11, Issue 3

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    Author Tags

    1. Dynamic Bayesian networks
    2. TDM
    3. sequential circuits

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    • (2014)Symbolic Analysis of Programmable Logic ControllersIEEE Transactions on Computers10.1109/TC.2013.12463:10(2563-2575)Online publication date: 1-Oct-2014
    • (2009)Probabilistic error modeling for nano-domain logic circuitsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2008.200316717:1(55-65)Online publication date: 1-Jan-2009
    • (2007)Probabilistic error modeling for sequential logic2007 7th IEEE Conference on Nanotechnology (IEEE NANO)10.1109/NANO.2007.4601266(616-620)Online publication date: Aug-2007
    • (2006)A timing-aware probabilistic model for single-event-upset analysisIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2006.88416514:10(1130-1139)Online publication date: 1-Oct-2006

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