面向数据中心的直接到芯片液体冷却生成设计
Generative Design for Direct-to-Chip Liquid Cooling for Data Centers
AI总结：
本文提出一种生成设计框架，用于合成NVIDIA GB200 Grace Blackwell超芯片的冷却通道几何结构，通过物理建模与反应扩散过程结合，优化冷却性能，降低芯片温度。
摘要：
人工智能工作负载的快速增长推动了数据中心功率密度的提升，增加了对先进热管理的需求。直接到芯片的液体冷却可以在源头高效去除热量，但许多冷板通道布局仍为启发式设计，未针对现代异构封装的强非均匀温度分布进行优化。本文提出了一种生成设计框架，用于合成NVIDIA GB200 Grace Blackwell超芯片的冷却通道几何结构。基于物理的有限差分热模型提供快速的稳态温度预测，并为约束反应扩散过程提供空间热反馈，该过程生成新的通道拓扑结构，同时强制执行入口/出口和组件约束。通过在闭环中迭代通道生成和热评估，该方法自然地将冷却能力重新分配到高功率区域并抑制热点的形成。与基线平行通道设计相比，所得到的通道在平均温度上降低了超过5摄氏度，在最大温度上降低了超过35摄氏度。总体而言，结果表明，将生成算法与轻量级物理建模相结合可以显著提高直接到芯片液体冷却性能，支持更可持续的AI计算扩展。
英文摘要：
Rapid growth in artificial intelligence (AI) workloads is driving up data center power densities, increasing the need for advanced thermal management. Direct-to-chip liquid cooling can remove heat efficiently at the source, but many cold plate channel layouts remain heuristic and are not optimized for the strongly non-uniform temperature distribution of modern heterogeneous packages. This work presents a generative design framework for synthesizing cooling channel geometries for the NVIDIA GB200 Grace Blackwell Superchip. A physics-based finite-difference thermal model provides rapid steady-state temperature predictions and supplies spatial thermal feedback to a constrained reaction-diffusion process that generates novel channel topologies while enforcing inlet/outlet and component constraints. By iterating channel generation and thermal evaluation in a closed loop, the method naturally redistributes cooling capacity toward high-power regions and suppresses hot-spot formation. Compared with a baseline parallel channel design, the resulting channels achieve more than a 5 degree Celsius reduction in average temperature and over 35 degree Celsius reduction in maximum temperature. Overall, the results demonstrate that coupling generative algorithms with lightweight physics-based modeling can significantly enhance direct-to-chip liquid cooling performance, supporting more sustainable scaling of AI computing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
