Regression-based RTL power models for controllers

Abstract

Power consumption of the controller of an RTL design, though typically smaller than that of the datapath, cannot be neglected. Controller power modeling is a more challenging task than that of datapath modules, because the controller is usually specified in an abstract fashion that has no structural relationship with its final implementation. This paper presents an extensive experimental study on pre.state assignment power models. Starting from a large set of controllers, we study the relation between power and static parameters, such as n~.mber of inputs and outputs, as well as dynamic parameters, such as input and output switching activity. We then quentin the loss of accuracy caused by the high level of abstraction of the models, and we show how previously published results, obtained under favorable ex. perimental conditions, have underestimated model inaccuracy. Finally, we perform extensive exploration on a large number of alternative macro-model structures, and we select an optimal model equation.