发表机构
University of Michigan; University of Texas at Dallas（密歇根大学; 德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探索后训练量化（PTQ）在自回归天气模拟器（DLWP和FourCastNet）中的应用，验证其能在短程预报中保持定性有效的预测，为动态系统DL模型的量化优化提供首个基准。
AI 中文摘要
高分辨率数值天气预报（NWP）和数据同化（DA）的进展推动了模拟大气动力学的深度学习（DL）架构的发展。天气预报模拟器在从几天到次季节时间尺度的预报时效内，其预报质量可与基于物理的模型相媲美。这些模拟器通过自回归推理中的硬件加速矩阵乘法驱动，显著减少了NWP所需的计算时间和资源。GPU架构中矩阵乘法过程的优化为扩展到高分辨率领域提供了机会，并提供了开箱即用解决方案的实现。后训练量化（PTQ）已在多种DL架构中得到验证，能够在单位时间内加速并增加计算量，同时消耗更少的功率，从而支持在边缘硬件上的应用。在本研究中，我们研究了PTQ对用于全球尺度天气预报的预训练AI模拟器的影响。我们将PTQ算法应用于深度学习天气预报（DLWP）和FourCastNet（FCN）模型，作为地球物理流体动力学应用的概念验证。我们系统地研究了PTQ对短程预报时效内模拟器推理的影响。使用模拟量化对PTQ配置进行的评估表明，在短时间范围内可产生定性上有意义的预报。这些结果为自回归天气模拟器的PTQ提供了首个基准，并为动态系统DL模型的基于量化的优化奠定了基础。
英文摘要
Advancements in high-resolution numerical weather prediction (NWP) and data assimilation (DA) have shaped the developments in deep learning (DL) architectures emulating atmospheric dynamics. Emulators for weather forecasting exhibit forecast quality comparable to physics based models at forecast horizon scaling from few days to subseasonal time scales. The emulators are driven by hardware-accelerated matrix multiplication in autoregressive inferences, significantly reducing the computation time and resources required for NWP. Optimization of the matrix multiplication processes in GPU architectures provides opportunities to scale towards high-resolution domain, and offers implementation of out of the box solutions. Post-training quantization (PTQ) has been demonstrated across multiple DL architectures to accelerate and increase the number of computations in unit time while consuming less power, enabling applications on edge hardware. In this study, we investigate the effect of PTQ on pre-trained AI emulators for global-scale weather forecasting. We implement PTQ algorithms in Deep Learning Weather Prediction (DLWP) and FourCastNet (FCN) models as a proof of concept for geophysical fluid dynamics applications. We systematically investigate the effect of PTQ on emulator inferences over short-range forecast horizons. Evaluation of PTQ configurations using simulated quantization hints at qualitatively meaningful forecasts over short-time horizons. These results provide a first benchmark of PTQ for autoregressive weather emulators and a basis for quantization-based optimization of DL models for dynamical systems.
