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.

翻译：磁约束聚变装置中有源反馈控制对于缓解等离子体不稳定性并实现稳健运行至关重要。光学高速相机作为一种强大的非侵入式诊断工具，适用于此类应用。本研究在可编程逻辑门阵列（FPGA）硬件上原位处理速度超过10万帧/秒的快相机数据，以跟踪磁流体动力学（MHD）模式演化并实时生成控制信号。系统采用卷积神经网络（CNN）模型，利用相机图像预测$n$=1 MHD模式的振幅和相位，其精度优于其他经测试的非深度学习类方法。通过将该模型直接部署在高速相机诊断系统的标准FPGA读出硬件中，我们的模式跟踪系统实现了17.6$\mu$s的总触发至输出延迟，以及高达12万帧/秒的吞吐量。在高Beta托卡马克-延长脉冲（HBT-EP）实验装置上的这项研究，展示了基于FPGA的高速相机数据采集与处理系统，为基于机器学习的实时托卡马克诊断与控制提供了应用可能，并具备向其他科学领域推广的潜力。