An efficient estimation of the ROBDD's complexity
Integration, the VLSI Journal
Hierarchical Probabilistic Macromodeling for QCA Circuits
IEEE Transactions on Computers
Binary Decision Diagrams and neural networks
The Journal of Supercomputing
Applicability of feed-forward and recurrent neural networks to Boolean function complexity modeling
Expert Systems with Applications: An International Journal
Boolean function complexity and neural networks
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Satisfiability models for maximum transition power
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Prediction of area and length complexity measures for binary decision diagrams
Expert Systems with Applications: An International Journal
An efficient estimation of the ROBDD's complexity
Integration, the VLSI Journal
Bounded delay timing analysis and power estimation using SAT
Microelectronics Journal
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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, onISCASý89 benchmark circuits involving up to 10000 signals.