Parameterized RTL power models for combinational soft macros

We propose a new RTL power macromodel that is suitable for re-configurable, synthesizable soft-macros. The model is parameterized with respect to the input data size (i.e., bit-width), and can be automatically scaled with respect to different technology libraries and/or synthesis options. Scalability is obtained through a single additional characterization run, and does not require the disclosure of any intellectual property. The model is derived from empirical analysis of the sensitivity of power on input statistics, input data size and technology. The experiments prove that, with limited approximation, it is possible to de-couple the effects on power of these three factors. The proposed solution is innovative, since no previous macromodel supports automatic technology scaling, and yields estimation errors within 15%.