Real-time Thermal Map Estimation for AMD Multi-Core CPUs using Transformer

This paper presents a novel approach for real-time estimation of spatial thermal maps for the commercial AMD Ryzen 7 4800U 8-core microprocessor using a transformer-based machine learning method. The proposed method, called ThermTransformer, leverages real-time performance metrics of the AMD chip, provided by uProf 4.0, to accurately estimate transient thermal maps. These maps can be valuable for dynamic thermal, power, and reliability controls requiring higher accuracy. Unlike traditional Convolutional Neural Networks (CNN) designed for image data or Recurrent Neural Networks (RNN) suitable for transient data, ThermTransformer is based on a modified self-attention architecture. It takes time-series performance metrics information as input and directly generates transient thermal images. Our results demonstrate that this transformer-based method achieves the best of both worlds - surpassing CNN in prediction quality and performing well for transient data. Experimental results reveal that ThermTransformer achieves highly accurate predictions of power maps, with an RMSE of only 0.36 degrees C or 0.8% of the full-scale error. Additionally, it outperforms the recently proposed GAN-based thermal map estimation method, ThermGAN, by 1.66x and the LSTM-based thermal prediction method, RealMaps, by 6.09x in terms of accuracy on average. Furthermore, the proposed approach can be efficiently deployed on the target chip, providing real-time estimation with a speed as fast as 14ms.