Utilizing Predictors for Efficient Thermal Management in Multiprocessor SoCs

Conventional thermal management techniques are reactive in nature; that is, they take action after temperature reaches a predetermined threshold value. Such approaches do not always minimize and balance the temperature on the chip, and furthermore, control temperature at a noticeable performance cost. In this paper, the authors investigate how to use predictors for forecasting future temperature and workload dynamics, and propose proactive thermal management techniques for MultiProcessor System-on-Chips (MPSoCs). The predictors they study include AutoRegressive Moving Average (ARMA) modeling and look-up table based predictors. They implement several reactive and predictive thermal management techniques, and provide extensive evaluations on an UltraSPARC T1 system as well as on an architecture-level simulator.