Trace-driven steady-state probability estimation in FSMs with application to power estimation

This paper illustrates, analytically and quantitatively, the effect of high-order temporal correlations on steady-state and transition probabilities in finite state machines (FSMs). As the main theoretical contribution, we extend the previous work done on steady-state probability calculation in FSMs to account for complex spatiotemporal correlations which are present at the primary inputs when the target machine models real hardware and receives data from real applications. More precisely: (1) using the concept of constrained reachability analysis, the correct set of Chapman-Kolmogorov equations is constructed; and (2) based on stochastic complementation and iterative aggregation/disaggregation techniques, exact and approximate methods for finding the state occupancy probabilities in the target machine are presented. From a practical point of view, we show that assuming temporal independence or even using first-order temporal models is not sufficient due to the inaccuracies induced in steady-state and transition probability calculations. Experimental results show that, if the order of the source is underestimated, not only the set of reachable sets is incorrectly determined, but also the steady-state probability values can be more than 100% off from the correct ones. This strongly impacts the accuracy of the total power estimates that can be obtained via probabilistic approaches