Abstract
This paper proposes a new architecture-level thermal modeling method to address the emerging thermal related analysis and optimization problem for high-performance multi-core microprocessor design. The new approach builds the thermal behavioral models from the measured or simulated thermal and power information at the architecture level for multi-core processors. Compared with existing behavioral thermal modeling algorithms, the proposed method can build the behavioral models from given arbitrary transient power and temperature waveforms used as the training data. Such an approach can make the modeling process much easier and less restrictive than before, and more amenable for practical measured data. The new method is based on a subspace identification method to build the thermal models, which first generates a Hankel matrix of Markov parameters, from which state matrices are obtained through minimum square optimization. To overcome the overfitting problems of the subspace method, the new method employs an overfitting mitigation technique to improve model accuracy and predictive ability. Experimental results on a real quad-core microprocessor show that ThermSID is more accurate than the existing ThermPOF method. Furthermore, the proposed overfitting mitigation technique is shown to significantly improve modeling accuracy and predictability.