A learning-based autoregressive model for fast transient thermal analysis of chip-multiprocessors

Published

Conference Paper

Thermal issues have become critical roadblocks for the development of advanced chip-multiprocessors (CMPs). In this paper, we introduce a new angle to view transient thermal analysis - based on predicting thermal profile, instead of calculating it. We develop a systematic framework that can learn different thermal profiles of a CMP by using an autoregressive (AR) model. The proposed AR model can serve as a fast alternative for predicting the transient temperature of a CMP with reasonably good accuracy. Experimental results show that the proposed AR model can achieve approximately 113X speed-up over existing thermal profile estimation methods, while introducing an error of only 0.8°C on average. © 2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Juan, DC; Zhou, H; Marculescu, D; Li, X

Published Date

  • April 26, 2012

Published In

  • Proceedings of the Asia and South Pacific Design Automation Conference, Asp Dac

Start / End Page

  • 597 - 602

International Standard Book Number 13 (ISBN-13)

  • 9781467307727

Digital Object Identifier (DOI)

  • 10.1109/ASPDAC.2012.6165027

Citation Source

  • Scopus