Sequence compaction for probabilistic analysis of finite-state machines

Abstract

The objective of this paper is to provide an effective technique for accurate modeling of the external input sequences that affect the behavior of Finite State Machines (FSMs). The proposed approach relies on adaptive modeling of binary input streams as Markov sources of fixed-order. The input model itself is derived through a one-pass traversal of the input sequence and can be used to generate an equivalent sequence, much shorter in length compared to the original sequence. 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 a significant loss (less than 3% on average) in the accuracy of estimated values.