Efficient power estimation for highly correlated input streams
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
ISLPED '95 Proceedings of the 1995 international symposium on Low power design
Short circuit power consumption of glitches
ISLPED '96 Proceedings of the 1996 international symposium on Low power electronics and design
Statistical estimation of average power dissipation in sequential circuits
DAC '97 Proceedings of the 34th annual Design Automation Conference
Statistical estimation of average power dissipation in sequential circuits
DAC '97 Proceedings of the 34th annual Design Automation Conference
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
Multimode power modeling and maximum-likelihood estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Nanoelectronic circuits and systems
Nonuniform sampling delta converters: design methodology
EHAC'06 Proceedings of the 5th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications
Bounds on FSM Switching Activity
Journal of Signal Processing Systems
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In this paper, we present a new statistical technique for estimation of average power dissipation in digital circuits. The present parametric statistical technique estimates the average power based on the assumption that the power distribution can be characterized by a preassumed function. Large error can incur when the assumption is not met. On the other hand, the existing nonparametric technique, although accurate, is too conservative and requires a large sample size in order to achieve convergence. For a good tradeoff between simulation accuracy and computational efficiency, we propose a new nonparametric technique using the properties of the order statistics. It is generally applicable to any type of circuit irrespective of its power distribution function. Compared to the existing nonparametric technique, it is much more computationally efficient since it requires a much smaller sample size to achieve the same accuracy specification. This new technique is implemented in the distribution-independent power estimation tool (DIPE). DIPE is empirically demonstrated to be more robust and accurate than the parametric technique.