In this paper we present a methodology and techniques for generating cycle-accurate macro-models for RT-level power analysis. The proposed macro-model predicts not only the cycle-by-cycle power consumption of a module, but the power profile of the module over time. The proposed methodology consists of three steps: module equation form generation and variable selection, variable reduction, and population stratification. First order temporal correlations and spatial correlations of up to order 3 are considered to improve the estimation accuracy. Experimental results show that, the macro-models have 15 or less variables and exhibit <5
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