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Probabilistic modeling of dependencies during switching activity analysis

This paper addresses, from a probabilistic point of view, the issue of switching activity estimation in combinational circuits under the zero-delay model. As the main theoretical contribution, we extend the previous work done on switching activity estimation to explicitly account for complex spatiotemporal correlations which occur at the primary inputs when the target circuit receives data from real applications. More precisely, using lag-one Markov chains, two new concepts-conditional independence and signal isotropy-are brought into attention and based on them, sufficient conditions for exact analysis of complex dependencies are given. From a practical point of view, it is shown that the relative error in calculating the switching activity of a logic gate using only pairwise probabilities can be upper-bounded. It is proved that the conditional independence problem is NP-complete and thus, relying on the concept of signal isotropy, approximate techniques with bounded error are proposed for estimating the switching activity. Evaluations of the model and a comparative analysis on benchmark circuits show that node-by-node switching activities are strongly pattern dependent and therefore, accounting for spatiotemporal dependencies is mandatory if accuracy is a major concern.