Active feedback control in magnetic confinement fusion devices is desirable
to mitigate plasma instabilities and enable robust operation. Optical
high-speed cameras provide a powerful, non-invasive diagnostic and can be
suitable for these applications. In this study, we process fast camera data, at
rates exceeding 100kfps, on $\textit{in situ}$ Field Programmable Gate Array
(FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate
control signals in real-time. Our system utilizes a convolutional neural
network (CNN) model which predicts the $n$=1 MHD mode amplitude and phase using
camera images with better accuracy than other tested non-deep-learning-based
methods. By implementing this model directly within the standard FPGA readout
hardware of the high-speed camera diagnostic, our mode tracking system achieves
a total trigger-to-output latency of 17.6$\mu$s and a throughput of up to
120kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment
demonstrates an FPGA-based high-speed camera data acquisition and processing
system, enabling application in real-time machine-learning-based tokamak
diagnostic and control as well as potential applications in other scientific
domains.

本研究利用光学高速相机和基于 FPGA 硬件进行数据处理，实时跟踪磁流体力学模式演化并生成控制信号，通过卷积神经网络模型预测和其他测试方法相比，更准确地预测摄像机图像中的 MHD 模式的振幅和相位，实现了在高速相机诊断中的总触发到输出延迟为 17.6 微秒和最高 120kfps 的吞吐量。该研究在高贝塔托卡马克 - 扩展脉冲（HBT-EP）实验中展示了基于 FPGA 的高速相机数据采集和处理系统，为实时机器学习应用于托卡马克诊断和控制以及其他科学领域提供了潜在应用。