A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

Abstract

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