Yang Zhao, Yangou Ouyang, Xiao Ding, Hepeng Wang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yang Zhao, Hepeng Wang, Xiao Ding, Yangou Ouyang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

As multi-core processor technology advances and integration levels increase, effective chip temperature management significantly impacts performance and energy efficiency. However, in high power environments, relying on traditional dynamic thermal management methods (such as adjusting voltage and frequency) is no longer sufficient to effectively control and regulate the temperature of processors. This paper introduces a reinforcement learning modeling approach based on a core thermal model, which combines a more accurate processor state model with reinforcement learning techniques. It also utilizes an adaptive dueling DQN architecture, which helps agents learn more effective thermal-aware task scheduling during the training process. Compared to other methods, this approach reduces the average and peak temperatures by at least 2.06°C and 2.47°C respectively. Additionally, under stricter time constraints, processor performance is maintained while reducing core temperature.

Jinglong Gao, Xiao Ding, Yiming Cui, Jianbai Zhao, Hepeng Wang, Ting Liu, Bing Qin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.