Composite sequence compaction for finite-state machines using block entropy and high-order Markov models

The objective of this paper is to provide an effective technique far accurate modeling of the external input sequences that affect the behavior of Finite State Machines (FSMs). Based on the block entropy concept, we present a technique for identifying the order of variable-order Markov sources of information. Furthermore, using dynamic Markov modeling, we propose an effective approach to compact an initial sequence into a much shorter equivalent one. The compacted sequence, can be subsequently used with any available simulator to derive the steady-state and transition probabilities, and the total power consumption in the target circuit. As the results demonstrate, large compaction ratios of orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of estimated values.