Abstract-Technology scaling has caused the feature sizes to shrink continuously, whereas interconnects, unlike transistors, have not followed the same trend. Designing 3D stack architectures is a recently proposed approach to overcome the power consumption and delay problems associated with the interconnects by reducing the length of the wires going across the chip. However, 3D integration introduces serious thermal challenges due to the high power density resulting from placing computational units on top of each other. In this work, we first investigate how the existing thermal management, power management and job scheduling policies affect the thermal behavior in 3D chips. We then propose a dynamic thermally-aware job scheduling technique for 3D systems to reduce the thermal problems at very low performance cost. Our approach can also be integrated with power management policies to reduce energy consumption while avoiding the thermal hot spots and large temperature variations.

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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.

Temperature-induced reliability issues are among the major challenges for multicore architectures. Thermal hot spots and thermal cycles combine to degrade reliability. This research presents new reliability-aware job scheduling and power management approaches for chip multiprocessors. Accurate evaluation of these policies requires a novel simulation framework that can capture architecture-level effects over tens of seconds or longer, while also capturing thermal interactions among cores resulting from dynamic scheduling policies. Using this framework and a set of new thermal management policies, this work shows that techniques that offer similar performance, energy, and even peak temperature can differ significantly in their effects on the expected processor lifetime.

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