A Novel Frequency-Spatial Domain Aware Network for Fast Thermal Prediction in 2.5D ICs

• URL: http://arxiv.org/abs/2504.14237v1
• Date: Sat, 19 Apr 2025 09:16:30 GMT
• Title: A Novel Frequency-Spatial Domain Aware Network for Fast Thermal Prediction in 2.5D ICs
• Authors: Dekang Zhang, Dan Niu, Zhou Jin, Yichao Dong, Jingweijia Tan, Changyin Sun,
• Abstract summary: We propose a novel frequency-spatial dual domain aware prediction network (FSA-Heat) for fast and high-accuracy thermal prediction in 2.5D ICs.<n>It integrates high-to-low frequency and spatial domain encoder (FSTE) module with frequency domain cross-scale interaction module (FCIFormer) to achieve high-to-low frequency and global-to-local thermal dissipation feature extraction.
• Score: 11.360423662589545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the post-Moore era, 2.5D chiplet-based ICs present significant challenges in thermal management due to increased power density and thermal hotspots. Neural network-based thermal prediction models can perform real-time predictions for many unseen new designs. However, existing CNN-based and GCN-based methods cannot effectively capture the global thermal features, especially for high-frequency components, hindering prediction accuracy enhancement. In this paper, we propose a novel frequency-spatial dual domain aware prediction network (FSA-Heat) for fast and high-accuracy thermal prediction in 2.5D ICs. It integrates high-to-low frequency and spatial domain encoder (FSTE) module with frequency domain cross-scale interaction module (FCIFormer) to achieve high-to-low frequency and global-to-local thermal dissipation feature extraction. Additionally, a frequency-spatial hybrid loss (FSL) is designed to effectively attenuate high-frequency thermal gradient noise and spatial misalignments. The experimental results show that the performance enhancements offered by our proposed method are substantial, outperforming the newly-proposed 2.5D method, GCN+PNA, by considerable margins (over 99% RMSE reduction, 4.23X inference time speedup). Moreover, extensive experiments demonstrate that FSA-Heat also exhibits robust generalization capabilities.

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