Accurate Runtime Thermal Prediction Scheme for 3D NoC Systems with Noisy Thermal Sensors

Thermal sensor noise has great impact on the efficiency and effectiveness of a dynamic thermal management (DTM) strategy. Conventional reactive thermal management techniques suffer significant performance degradation due to the pessimistic reaction. In this paper, to address the problem of forecasting temperatures based on noisy thermal readings, we propose a Kalman predictor based runtime thermal prediction scheme, which can predict temperatures N step ahead. An activity-based power model for 3D NoC power estimation is also proposed; the model is an essential prerequisite of accurate temperature predictions. Besides that, we propose a distributed multi-input single-output (MISO) thermal model for 3D NoC systems, which reduces the computational complexity of temperature updating from m(2) to m compared with the centralized multi-input multi-output (MIMO) model for the system with m units. The experimental results show that the proposed prediction scheme reduces the mean absolute error (MAE) by 42.8%-72.6% compared with the auto-regressive (AR) based prediction scheme.