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

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.