Stratified Random Sampling for Power Estimation
Cunsheng Ding, Cheng-Ta Hsieh, Q. M. Jonathan Wu, Massoud Pedram · 1996
In this paper, we present new statistical sampling techniques for performing power estimation at the circuit level. These techniques first transform the power estimation problem to a survey sampling problem, then apply stratified random sampling to improve the efficiency of sampling. The stratification is based on a low-cost predictor, such as zero delay power estimates. We also propose a two-stage stratified sampling technique to handle very long initial sequences. Experimental results show that the efficiency of stratified random sampling and two-stage stratified sampling techniques are 3-10X higher than that of simple random sampling and the Markov-based Monte Carlo simulation techniques.