Accurate On-Chip Temperature Sensing for Multicore Processors Using Embedded Thermal Sensors

Abstract

Thermal issues seriously restrict the quality, reliability, and lifetime of semiconductor chips. To prevent thermal runaway situations, modern processors deploy numerous on-die thermal sensors to collect temperature information, which is then used to guide dynamic thermal management (DTM) mechanisms. Accurate on-chip temperature information is critical for DTM as temperature overestimation will degrade the performance by an unnecessary invocation of thermal control mechanisms, and underestimation will result in the reliability issues of the systems. In this article, two effective techniques are proposed to achieve an accurate on-chip temperature sensing. The first technique is a synergistic calibration method for forecasting the actual temperatures of noisy thermal sensors. Second, we propose a full thermal characterization technique based on convolutional neural networks (CNNs) to accurately recover the entire thermal maps by using a limited number of thermal sensors. In a realistic scenario, these two techniques can be used in combination to provide a more accurate thermal monitoring. By utilizing the sophisticated infrared imaging setup, the effectiveness of the proposed techniques is validated on a real 45-nm AMD quad-core chip. The simulation results show that the methods achieve significant improvements compared with existing techniques in the literature. The successful implementation of the proposed methods will significantly improve the efficiency of DTM.