Dependency preserving probabilistic modeling of switching activity using bayesian networks
Sanjukta Bhanja, Nagarajan Ranganathan · 2001
We propose a new switching probability model for combinational circuits using aLogic-Induced-Directed-Acyclic-Graph(LIDAG) and prove that such a graph corresponds to aBayesian Networkguaranteed to map all the dependencies inherent in the circuit. This switching activity can be estimated by capturing complex dependencies (spatio-temporal 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).