Adaptive models for input data compaction for power simulators

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 the spatiotemporal correlations which occur during power estimation in both combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without a significant loss (less than 5% on average) in the accuracy of the power estimates.