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