Statistical estimation of the switching activity in digital circuits
DAC '94 Proceedings of the 31st annual Design Automation Conference
Stratified random sampling for power estimation
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Improving the efficiency of Monte Carlo power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on the 11th international symposium on system-level synthesis and design (ISSS'98)
On mixture density and maximum likelihood power estimation via expectation-maximization
ASP-DAC '00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference
Least-square estimation of average power in digital CMOS circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Sequential Circuit Design Using Synthesis and Optimization
ICCD '92 Proceedings of the 1991 IEEE International Conference on Computer Design on VLSI in Computer & Processors
Statistical estimation of average power dissipation using nonparametric techniques
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A multilevel engine for fast power simulation of realistic input streams
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Sequential algorithm for low-power encoding internal states of finite state machines
Journal of Computer and Systems Sciences International
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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.