Synergistic Calibration of Noisy Thermal Sensors Using Smoothing Filter-Based Kalman Predictor

Embedded thermal sensors are very susceptible to a variety of noise sources, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of dynamic thermal management (DTM). In this paper, a smoothing filter-based Kalman prediction technique is proposed to estimate the accurate temperatures of noisy sensors. On this basis, a multi-sensor synergistic calibration algorithm is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an AMD quadcore processor in real-time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the synergistic calibration scheme can achieve an average reduction of the root-mean-square error (RMSE) by 75.9% compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 21.6%. These results clearly demonstrate that if our approach is used to perform the temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.