Probabilistic analysis of large finite state machines
DAC '94 Proceedings of the 31st annual Design Automation Conference
Power estimation methods for sequential logic circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Stochastic sequential machine synthesis targeting constrained sequence generation
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Stream synthesis for efficient power simulation based on spectral transforms
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
Trace-driven steady-state probability estimation in FSMs with application to power estimation
Proceedings of the conference on Design, automation and test in Europe
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The objective of this paper is to provide aneffective technique for accurate modeling of the externalinput sequences that affect the behavior of Finite StateMachines (FSMs). The proposed approach relies on adaptivemodeling of binary input streams as Markov sources of fixed-order.The input model itself is derived through a one-passtraversal of the input sequence and can be used to generatean equivalent sequence, much shorter in length compared tothe original sequence. The compacted sequence can besubsequently used with any available simulator to derive thesteady-state and transition probabilities, and the total powerconsumption in the target circuit. As the results demonstrate,large compaction ratios of orders of magnitude can beobtained without a significant loss (less than 3% on average)in the accuracy of estimated values.