Adaptive models for input data compaction for power simulators

This paper presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex spatiotemporal correlations which occur during power estimation both in combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without significant loss (less than 5% on average) in the accuracy of power estimates.