Skip to Main Content
It is known that circuits exhibit multiple modes of power consumption due to various factors such as the presence of many feedback (or sequential) elements, RAM, large size, etc. Previous power-estimation techniques have largely ignored this fact. For example, Monte Carlo simulation-based power estimators tend to produce estimates for the average power consumption that corresponds only to the most probable power mode of the circuit. This can be a cause for trouble later in the design step. The aim of this paper is twofold. First, an algorithm is proposed that estimates the total number of power modes of a circuit based on simulated data. This is then followed by a maximum-likelihood estimation procedure that produces the average values of the power modes along with their probabilities of occurrence. Theoretical ideas are supported by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The proposed method is shown to perform well by capturing the multiple power modes for both large and small circuits even when the number of simulated samples is small while the Monte Carlo estimator does not. We conclude with a note that the proposed method is also applicable to other model selection problems in VLSI.