Node sampling: a robust RTL power modeling approach

Abstract

We propose a robust RTL power modeling methodology for functional units. Our models are consistently accurate over a wide range of input statistics, they are automatically constructed and can provide pattern-by-pattern power estimates. An additional desirable feature of our modeling methodology is the capability of accounting for the impact of technology variations, library changes and synthesis tools. Our methodology is based on the concept of node sampling, as opposed to more traditional approaches based on input sampling. We analyze the theoretical properties of node sampling and we formally show that it is a statistically sound approach. The superior robustness of our method is due to its limited dependency on pattern based characterization.