TherMapNet Attention-Guided Runtime Full-Chip Thermal Map Prediction from Performance Metrics

Abstract:Runtime thermal management of high-performance chips depends on fast and accurate full-chip thermal maps. Conventional simulators typically estimate power traces from performance metrics first, which adds overhead. This work proposes TherMapNet, an attention-guided thermal simulator that predicts full-chip thermal maps directly from performance metrics. A Transformer encoder captures temporal evolution by treating the time series of each metric as a token, improving modeling of dynamic workloads. A CNN then extracts fine-grained spatial features. For the CNN, a dual-branch channel-spatial attention convolution module (DACM) and a triplet loss are used to improve spatial learning and reconstruction accuracy. TherMapNet is applied to a multi-core CPU (AMD Ryzen 7 4800U) and a many-core GPU (NVIDIA GeForce RTX 4060). Experiments show that it outperforms prior thermal simulators, with RMSE below 0.26 C and inference under 2.4 ms on an NVIDIA GeForce RTX 3090 GPU. These results indicate that TherMapNet can support high-quality runtime thermal management of modern multi-core chips.

IEEE conference format, 8 figures, 2 tables. Submitted to IEEE ICCD. Corresponding author: Lin Jiang

Hardware Architecture (cs.AR); Machine Learning (cs.LG)

B.7.1; C.4; I.2.6

arXiv:2608.21887 [cs.AR]

(or arXiv:2608.21887v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2608.21887 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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