神经算子在虚拟传感中的节能运行

中文摘要 AI 辅助

虚拟传感从变化的观测中反复重建物理场，通常是在固定几何体上进行。我们研究了共享空间计算如何在保留所选检查点及其评估预测的同时，降低这些更新的能耗。在换热器服务中，相对于图重放，标准编译器冻结和显式主干复用带来的运行能耗降低相似：在每秒一个请求时约为1%，在每秒四十个请求时约为20%。在固定时钟的15瓦模式下，复用与图重放相结合，完成相同请求序列的能耗比急切执行低22.0%至22.5%，包括准备和等待时间。DeepONet和傅里叶神经算子（FNO）控制区分了可复用算术和启动开销的影响。准备、工件构建和工作者替换增加了重复推理之外的成本。这些结果将算子结构与运行能耗联系起来，并表明更新频率和执行生命周期决定了计算复用在物理场虚拟传感中的收益。

英文摘要

Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computation reduces the energy of these updates while retaining the selected checkpoint and its evaluated predictions. In a heat-exchanger service, standard compiler freezing and explicit trunk reuse give similar operating energy reductions relative to graph replay: approximately 1% at one request per second and 20% at forty requests per second. In 15 W mode with fixed clocks, reuse with graph replay completes the same request sequence with 22.0 to 22.5% less energy than eager execution, including preparation and waiting. DeepONet and Fourier neural operator (FNO) controls distinguish the effects of reusable arithmetic and launch overhead. Preparation, artifact construction, and worker replacement add costs outside repeated inference. These results connect operator structure to operating energy and show how update frequency and execution lifetime govern the benefit of computation reuse in physical-field virtual sensing.

发表机构

• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
• Indian Institute of Technology Delhi（印度理工学院德里分校）
• National Center for Supercomputing Applications（国家超级计算应用中心）

机构由 AI 辅助整理，请以论文原文为准。