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This paper describes LP-DSM, which is an algorithm used for efficient library characterization in high-level power estimation. LP-DSM characterizes the power consumption of building blocks using the entropy of primary inputs and primary outputs. The experimental results showed that over a wide range of benchmark circuits implemented using full custom design in 0.35-/spl mu/m 3.3 V CMOS process the statistical performance (mean and maximum error) of LP-DSM is comparable or sometimes better than most of the published algorithms. Moreover, it was found that LP-DSM has the lowest prediction sum of squares, which makes it an efficient tool for power prediction. Furthermore, the complexity of the LP-DSM is linear in relation to the number of primary inputs (O(NI)), whereas state of the art published library characterization algorithms have a complexity of O(NI/sup 2/).