Any-time probabilistic switching model using bayesian networks

Shiva Shankar Ramani, Sanjukta Bhanja
<span title="">2004</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qcoemsk4lfgznflartfnzb5rhy" style="color: black;">Proceedings of the 2004 international symposium on Low power electronics and design - ISLPED &#39;04</a> </i> &nbsp;
Modeling and estimation of switching activities remain to be important problems in low-power design and fault analysis. A probabilistic Bayesian Network based switching model can explicitly model all spatio-temporal dependency relationships in a combinational circuit, resulting in zero-error estimates. However, the space-time requirements of exact estimation schemes, based on this model, increase with circuit complexity [1, 2] . This paper explores a non-simulative, Importance Sampling based,
more &raquo; ... obabilistic estimation strategy that scales well with circuit complexity. It has the any-time aspect of simulation and the input pattern independence of probabilistic models.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1013235.1013263">doi:10.1145/1013235.1013263</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/islped/RamaniB04.html">dblp:conf/islped/RamaniB04</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g2h4kxmdkrfide25oimavpympu">fatcat:g2h4kxmdkrfide25oimavpympu</a> </span>
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