Architecture-level thermal behavioral characterization for multi-core microprocessors

In this paper, we investigate a new architecture-level thermal characterization problem from behavioral modeling perspective to address the emerging thermal related analysis and optimization problems for high-performance multi-core microprocessor design. We propose a new approach, called ThermPOF, to build the thermal behavioral models from the measured architecture thermal and power information. ThermPOF first builds the behavioral thermal model using generalized pencil-of-function (GPOF) method. And then to effectively model transient temperature changes, we proposed two new schemes to improve the GPOF. First we apply logarithmic-scale sampling instead of traditional linear sampling to better capture the temperature changing characteristics. Second, we modify the extracted thermal impulse response such that the extracted poles from GPOF are guaranteed to be stable without accuracy loss. To further reduce the model size, Krylov subspace based model order reduction is performed to reduce the order of the models in the state-space form. Experimental results on a practical quad-core microprocessor show that generated thermal behavioral models match the measured data very well.