stratified random sampling for power estimation
In this paper, we propose a statistical power evaluation framework at the RT-level. We first discuss the power macro-modeling formulation, and then propose a simple random sampling technique to alleviate the the overhead of macro-modeling during RTL simulation. Next, we describe a regression estimator to reduce the error of the macro-modeling approach. Experimental results indicate that the execution time of the simple random sampling combined with power macro-modeling is 50 X lower than that of conventional macro-modeling while the percentage error of regression estimation combined with power macro-modeling is 16 X lower than that of conventional macro-modeling. Hence, we provide the designer with options to either improve the accuracy or the execution time when using power macro-modeling in the context of RTL simulation.
random sampling power estimates paper statistics evaluation power macro-modeling formulation rtl simulation simulation regression regression estimator errors experimentation timing design accuracy context  framework  macros statistical sampling statistical analysis rt-level power evaluation model regression analysis - power estimation