By Topic

Wire-length prediction using statistical techniques

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

5 Author(s)
J. L. Wong ; California Univ., Los Angeles, CA, USA ; A. Davoodi ; V. Khandelwal ; A. Srivastava
more authors

We address the classic wire-length estimation problem and propose a new statistical wire-length estimation approach that captures the probability distribution function of net lengths after placement and before routing. The wire-length prediction model was developed using a combination of parametric and non-parametric statistical techniques. The model predicts not only the length of the net using input parameters extracted from the floorplan of a design, but also probability distributions that a net with given characteristics obtained after placement will have a particular length. The model is validated using both learn-and-test and resubstitution techniques. The model can be used for a variety of purposes, including the generation of a large number of statistically sound and therefore realistic instances of designs. We applied the net models to the probabilistic buffer insertion problem and obtained substantial improvement in net delay after routing.

Published in:

Computer Aided Design, 2004. ICCAD-2004. IEEE/ACM International Conference on

Date of Conference:

7-11 Nov. 2004