By Topic

Evaluating Statistical Power Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jason Cong ; Department of Computer Science, University of California, Los Angeles, CA, USA ; Puneet Gupta ; John Lee

In response to the increasing variations in integrated-circuit manufacturing, the current trend is to create designs that take these variations into account statistically. In this paper, we quantify the difference between the statistical and deterministic optima of leakage power while making no assumptions about the delay model. We develop a framework for deriving a theoretical upper bound on the suboptimality that is incurred by using the deterministic optimum as an approximation for the statistical optimum. We show that for the mean power measure, the deterministic optima is an excellent approximation, and for the mean plus standard deviation measures, the optimality gap increases as the amount of inter-die variation grows, for a suite of benchmark circuits in a 45 nm technology. For large variations, we show that there are excellent linear approximations that can be used to approximate the effects of variation. Therefore, the need to develop special statistical power optimization algorithms is questionable.

Published in:

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  (Volume:29 ,  Issue: 11 )