The goal of thermal management is to meet maximum operating temperature constraints, while at the same time tracking timevarying performance requirements. Current approaches avoid thermal violations by forcing abrupt operating points changes (e.g. processor shutdown), which cause sharp performance degradation. In this paper we aim at achieving a smooth thermal control action, that minimizes the variance of performance tracking error. We formulate this problem as a discrete-time optimal control problem, which can be solved using the theory and computational tools developed in the field of model-predictive control. Our optimization process considers the thermal profile of the system, its evolution over time, and time-varying workload requirements. Experimental results show that the proposed approach offers significant thermal balancing improvements over previous methods.
Multicore Thermal Management with Model Predictive Control
BENINI, LUCA;
2009
Abstract
The goal of thermal management is to meet maximum operating temperature constraints, while at the same time tracking timevarying performance requirements. Current approaches avoid thermal violations by forcing abrupt operating points changes (e.g. processor shutdown), which cause sharp performance degradation. In this paper we aim at achieving a smooth thermal control action, that minimizes the variance of performance tracking error. We formulate this problem as a discrete-time optimal control problem, which can be solved using the theory and computational tools developed in the field of model-predictive control. Our optimization process considers the thermal profile of the system, its evolution over time, and time-varying workload requirements. Experimental results show that the proposed approach offers significant thermal balancing improvements over previous methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
