Abstract-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.
Coskun et al. investigated how to use predictors for forecasting future temperature and workload dynamics, and propose proactive thermal management techniques for multiprocessor system-on-chips(MPSoCs) REF .
Utilizing Predictors for Efficient Thermal Management in Multiprocessor SoCs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Computer Science