The thermal wall for many-core systems on-chip calls for advanced management techniques to maximize performance, while capping temperatures. Distributed and compact thermal models are a cornerstone for such techniques. System identification methodologies allow to extract models directly from the target device thermal response. Unfortunately, standard Auto-Regressive eXogenous models and Least Squares techniques cannot effectively tackle both model approximation and measurement noise typical of real systems. In this work, we propose a novel distributed identification strategy to derive distributed interacting thermal models. The presentedmethod can copewith both process noise and temperature sensor noise affecting inputs and outputs of the adopted models. Online and offline versions are presented, and issues related to model order, sampling time and input stimuli are addressed. The proposed method is applied to the Intels Single-chip-Cloud-Computer many-core prototype.
Diversi, R.; Tilli, A.; Bartolini, A.; Beneventi, F.; Benini, L., “Bias-Compensated Least Squares Identification of Distributed Thermal Models for Many-Core Systems-on-Chip,” Circuits and Systems I: Regular Papers, IEEE Transactions on , vol.61, no.9, pp.2663,2676, Sept. 2014 doi: 10.1109/TCSI.2014.2312495