Estimation of power dissipation using a novel power macromodeling technique

In this paper, we develop a novel technique based on Markov chains to accurately estimate power sensitivities to primary inputs in CMOS sequential circuits. A key application of power sensitivities is to construct a complicated power surface in the specification-space so as to easily obtain the power dissipation under any distribution of primary inputs, thereby offering an effective power macromodel for high-level power estimation. We demonstrate that such a power surface can be approximated by only a limited number of representative points. This benefit dramatically reduces the CPU and memory requirements. We have verified the feasibility and accuracy of the new technique to estimate power sensitivities on a large number of sequential benchmark circuits. Results on the power dissipation under different distributions of primary inputs demonstrate the efficiency and effectiveness of our power macromodeling technique.