Regression-based RTL power modeling

Register-transfer level (RTL) power estimation is a key feature for synthes is-based design flows. The main challenge in establishing a sound RTL power estimation methodology is the construction of accurate, yet efficient, mod els of the power dissipation of functional macros. Such models should be au tomatically built, and should produce reliable average power estimates. In this paper we propose a general methodology for building and tuning RTL power models. We address both hard macros (presynthesized functional blocks ) and soft macros (functional units for which only a synthesizable HDL desc ription is provided). We exploit linear regression and its nonparametric ex tensions to express the dependency of power dissipation on input and output activity. Bottom-up off-line characterization of regression-based power ma cromodels is discussed in detail. Moreover, we introduce a low overhead on- line characterization method for enhancing the accuracy of off-line charact erization.