Cascaded bayesian inferencing for switching activity estimation with correlated inputs, Accepted for publication

In this work, we investigate the estimation of switching activity in VLSI cir-cuits using a graphical probabilistic model based on Cascaded Bayesian Net-works (CBN’s). First, we develop a theoretical analysis for Bayesian inferenc-ing of switching activity and then derive upper bounds for certain circuit param-eters which, in turn, are useful in establishing the cascade structure of the CBN model. We formulate an elegant framework for maintaining probabilistic con-sistency in the interfacing boundaries across the CBN’s during the inference process using a tree-dependent (TD) probability distribution function. A TD distribution is an approximation of the true joint probability function over the switching variables, with the constraint that the underlying Bayesian network representation is a tree. The tree approximation of the true joint probability function can be arrived at by using a Maximum Weight Spanning Tree (MWST) built using pairwise mutual information about the switching occurring at pairs of signal lines on the boundary. Further, we show that the proposed TD dis-tribution function can be used to model correlations among the primary inputs which is critical for accuracy in modeling of switching activity. Experimental results for ISCAS circuits are presented to illustrate the efficacy of the proposed CBN models. I