Dependency Preserving Probabilistic Modeling of Switching Activity using Bayesian Networks

S. Bhanja and N. Ranganathan, “Dependency Preserving Probabilistic Modeling of Switching Activity using Bayesian Networks,” IEEE/ACM Design Automation Conference (DAC), pp209-214, 2001.


@INPROCEEDINGS{935506,
title={Dependency preserving probabilistic modeling of switching activity using Bayesian networks},
author={Bhanja, S. and Ranganathan, N.},
booktitle={Design Automation Conference, 2001. Proceedings},
year={2001},
month={},
volume={},
number={},
pages={ 209-214},
abstract={ We propose a new switching probability model for combinational circuits using a logic-induced-directed-acyclic-graph (LIDBG) and prove that such a graph corresponds to a Bayesian network guaranteed to map all the dependencies inherent in the circuit. This switching activity can be estimated by capturing complex dependencies (spatiotemporal and conditional) among signals efficiently by local message-passing based on the Bayesian networks. Switching activity estimation of ISCAS and MCNC circuits with random input streams yield high accuracy (average mean error=0.002) and low computational time (average time=3.93 seconds).},
keywords={ belief networks, combinational circuits, logic CAD, probability 3.93 s, Bayesian network, Bayesian networks, ISCAS circuits, MCNC circuits, combinational circuits, computational time, conditional complex dependencies, dependency preserving probabilistic modeling, local message-passing, logic-induced-directed-acyclic-graph, mean error, spatiotemporal complex dependencies, switching activity},
doi={},
ISSN={0738-100X }, }