Regression-based RTL power modeling

Abstract

Register-transfer level (RTL) power estimation is a key feature for synthesis-based design flows. The main challenge in establishing a sound RTL power estimation methodology is the construction of accurate, yet efficient, models of the power dissipation of functional macros. Such models should be automatically 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 description is provided). We exploit linear regression and its nonparametric extensions to express the dependency of power dissipation on input and output activity. Bottom-up off-line characterization of regression-based power macromodels is discussed in detail. Moreover, we introduce a low overhead on-line characterization method for enhancing the accuracy of off-line characterization. © 2000 ACM.