By Topic

Classifying circuit performance using active-learning guided support vector machines

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

2 Author(s)
Honghuang Lin ; Texas A&M Univ., College Station, TX, USA ; Peng Li

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines, the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric support vector machine classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using three case studies: PLL lock-time verification, SRAM yield analysis and prediction of chip peak temperature using a limited number of on-chip temperature sensors.

Published in:

2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Date of Conference:

5-8 Nov. 2012