Parameterized RTL Power Models for Soft Macros

We propose a new power macromodel for usage in the context of register-transfer level (RTL) power estimation. The model is suitable for reconfigurable, synthesizable, soft macros because it is parameterized with respect to the input data size (i.e., bit width) and can also be automatically scaled with respect to different technology libraries and/or synthesis options. The power model is precharacterized once and for all for each soft macro and then adapted to each specific instance by means of a single additional experiment to be performed by the end user. No intellectual-property disclosure is required for model scaling. The proposed model is derived from empirical analysis of the sensitivity of power consumption on input statistics, input data size, and technology. The experiments prove that with limited approximation, it is possible to decouple the effects on power of these three factors. The proposed solution is innovative since no previous macromodel supports automatic technology scaling and yields average estimation errors around 10%.