Bias-Compensated Least Squares Identification of Distributed Thermal Models for Many-Core Systems-on-Chip

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 presented method can cope with 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 Intel's Single-chip-Cloud-Computer many-core prototype.