PredictNcool: Leakage Aware Thermal Management for 3D Memories Using a Lightweight Temperature Predictor

Recent research on mitigating thermal problems in 3D memories has covered reactive strategies that reduce memory power consumption, and thereby, performance, when the memory temperature reaches the maximum operating limit. Such techniques could benefit from temperature prediction and avoid unnecessary invocations and state transitions of the thermal management strategy. We develop an accurate steady state temperature predictor for thermal management of 3D memories. We utilize the symmetries in the floorplan, along with other design insights, to reduce the predictor’s model parameters, making it lightweight and suitable for runtime thermal management. Using the temperature prediction, we introduce PredictNcool, a proactive thermal management strategy to reduce application runtime and memory energy. We compare PredictNcool with two recent thermal management strategies and our experiments show that the proposed optimization results in performance improvements of 28% and 5%, and memory subsystem energy reductions of 38% and 12% (on average).