NDLI: Regression-based RTL power models for controllers

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Regression-based RTL power models for controllers

Content Provider	ACM Digital Library
Author	Surmei, Mihai Benini, Luca Poncino, Massimo Bogliolo, Alessandro Macii, Enrico
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 number of inputs and outputs, as well as dynamic parameters, such as input and output switching activity. We then quantify the loss of accuracy caused by the high level of abstraction of the models, and we show how previously published results, obtained under favorable experimental 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.
Starting Page	147
Ending Page	152
Page Count	6
File Format	PDF
ISBN	1581132514
DOI	10.1145/330855.331025
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-03-02
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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