A Stimulus-free Graphical Probabilistic Switching Model for Sequential Circuits using Dynamic Bayesian Networks

We propose a novel, non-simulative, 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; it is an exact model for the underlying conditional independencies. The attractive feature of this graphical representation of the joint probability function is that not only does it make the dependency relationships amongst the 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 the mean error values, on ISCAS’89 benchmark circuits involving up to 10000 signals.