Towards NN-based Online Estimation of the Full-Chip Temperature and the Rate of Temperature Change.

We propose a novel technique to estimate at run-time both the dynamic thermal map of the whole chip and the rate of temperature change. Knowledge of the current temperature is crucial for thermal management. Additional knowledge of the rate of temperature change allows for predictions of temperatures in the near future, and, therefore, enables proactive management. However, neither is achievable with existing thermal sensors due to their limited number. Our technique is based on a neural network (NN) to predict the rate of temperature change based on performance counter readings and the current estimate of the thermal map. The thermal map is then updated based on the prediction. At design-time, we create training data for the NN by recording performance counters and the dynamic thermal map during the execution of mixed workloads. The thermal map is recorded with the help of an infrared (IR) camera. At run-time, our technique requires only performance counter readings. Our technique predicts temperature changes accurately. However, absolute temperature estimation suffers from instability.