High-Resolution Thermal Maps Extraction of Multi-Core Processors Based on Convolutional Neural Networks

Thermal issues are a major concern in high-end computing systems as they severely constrain the performance and shorten the lifetime of integrated circuits. Using embedded on-die thermal sensors, state-of-the-art processors widely employ dynamic thermal management (DTM) mechanisms to prevent thermal runaway situations in multi-core architectures. Full thermal characterization is particularly useful for fine-grained thermal management techniques, examples of which include per-core workload scheduling, and voltage and frequency scaling. In this paper, a new direction for full thermal reconstruction of multi-core processors is proposed based on convolutional neural networks (CNNs) to precisely recover the overall thermal maps from a small number of sensors. The effectiveness of our method is verified on a real AMD quad-core processor. Experimental results indicate that the proposed method is capable of handling high-resolution thermal extraction, and achieves significant improvements compared to available techniques in the literature. The main contribution of this work is to demonstrate the ability of deep learning approaches for full thermal reconstruction of semiconductor chips. The success of our proposed method will assist DTM to achieve a more accurate thermal monitoring.