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Proactive temperature balancing for low cost thermal management in MPSoCs
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Thermal prediction and adaptive control through workload phase detection
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ACM Transactions on Design Automation of Electronic Systems (TODAES)
SCC thermal model identification via advanced bias-compensated least-squares
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DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Compact thermal modeling for packaged microprocessor design with practical power maps
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Conventional thermal management techniques are reactive, as they take action after temperature reaches a threshold. Such approaches do not always minimize and balance the temperature, and they control temperature at a noticeable performance cost. This paper investigates how to use predictors for forecasting temperature and workload dynamics, and proposes proactive thermal management techniques for multiprocessor system-on-chips. The predictors we study include autoregressive moving average modeling and lookup tables. We evaluate several reactive and predictive techniques on an UltraSPARC T1 processor and an architecture-level simulator. Proactive methods achieve significantly better thermal profiles and performance in comparison to reactive policies.