Bootstrap Monte Carlo With Adaptive Stratification for Power Estimation

Abstract

Monte Carlo approach for power estimation is based on the assumption that the samples of power are Normally distributed. However, the power distribution of a circuit is not always Normal in the real world. In this paper, the Bootstrap method is adopted to adjust the confidence interval and redeem the deficiency of the conventional Monte Carlo method. Besides, a new input sequence stratification technique for power estimation is proposed. The proposed technique utilizes a multiple regression method to compute the coefficient matrix of the indicator function for stratification. This new stratification technique can adaptively update the coefficient matrix and keep the population of input vectors in a better stratification status. The experimental results demonstrate that the proposed Bootstrap Monte Carlo method with adaptive stratification can effectively reduce the simulation time and meet the user-specified confidence level and error level.