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

2026-08-22

AI Summary

为解决高性能芯片运行时热管理问题，提出TherMapNet，利用Transformer和CNN直接从性能指标预测全芯片热图，提高准确性和速度。

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.

Problem

• runtime thermal management
• high-performance chips
• full-chip thermal maps
• performance metrics

Innovation

• attention-guided
• thermal simulator
• Transformer encoder
• dual-branch channel-spatial attention convolution module (DACM)
• triplet loss