TherMapNet：基于注意力机制的、从性能指标预测全芯片运行时热图的模型
TherMapNet Attention-Guided Runtime Full-Chip Thermal Map Prediction from Performance Metrics
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中文总结 AI 辅助
本研究提出TherMapNet模型，通过Transformer编码器与带DACM模块的CNN直接从性能指标预测全芯片热图，在两款芯片上的实验显示其精度与速度均优于现有热模拟器，可支撑现代多核芯片的运行时热管理。
中文摘要 AI 辅助
高性能芯片的运行时热管理依赖快速且准确的全芯片热图。传统模拟器通常需先从性能指标估算功耗轨迹，这会增加开销。本研究提出TherMapNet，一种注意力引导的热模拟器，可直接从性能指标预测全芯片热图。该模型采用Transformer编码器，将每个指标的时间序列视为一个token以捕捉时间演化，提升动态 workload 的建模能力；随后用CNN提取细粒度空间特征，其中CNN采用双分支通道-空间注意力卷积模块（DACM）和三元组损失函数，以优化空间学习与重建精度。将TherMapNet应用于AMD Ryzen 7 4800U多核CPU和NVIDIA GeForce RTX 4060众核GPU，实验表明其性能优于现有热模拟器：在NVIDIA GeForce RTX 3090 GPU上，均方根误差（RMSE）低于0.26℃，推理耗时不足2.4ms。这些结果表明TherMapNet可支持现代多核芯片的高质量运行时热管理。
英文摘要
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.
发表机构
- College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
