Architecture-Level Thermal Characterization for Multicore Microprocessors

This paper investigates a new architecture-level thermal characterization problem from a behavioral modeling perspective to address the emerging thermal related analysis and optimization problems for high-performance multicore microprocessor design. We propose a new approach, called ThermPOF, to build the thermal behavioral models from the measured or simulated thermal and power information at the architecture level. ThermPOF first builds the behavioral thermal model using the generalized pencil-of-function (GPOF) method. Owing to the unique characteristics of transient temperature changes at the chip level, we propose two new schemes to improve the GPOF. First, we apply a logarithmic-scale sampling scheme instead of the traditional linear sampling to better capture the temperature changing behaviors. 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, a Krylov subspace-based reduction method is performed to reduce the order of the models in the state-space form. Experimental results on a real quad-core microprocessor show that generated thermal behavioral models match the given temperature very well.