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：磁约束聚变装置中的主动反馈控制对于缓解等离子体不稳定性及实现稳健运行至关重要。光学高速相机提供了一种强大的非侵入式诊断手段，非常适合此类应用。本研究在$\textit{原位}$现场可编程门阵列（FPGA）硬件上处理速率超过100kfps的快速相机数据，以实时追踪磁流体动力学（MHD）模式演化并生成控制信号。我们的系统采用卷积神经网络（CNN）模型，该模型利用相机图像预测$n$=1 MHD模式的振幅与相位，其精度优于其他已测试的非深度学习方法。通过将该模型直接部署在高速相机诊断系统的标准FPGA读出硬件内，我们的模式追踪系统实现了17.6$\mu$s的总触发至输出延迟，以及高达120kfps的数据吞吐量。此项在High Beta Tokamak-Extended Pulse（HBT-EP）实验装置上进行的研究，展示了一套基于FPGA的高速相机数据采集与处理系统，为实时机器学习驱动的托卡马克诊断与控制提供了可行方案，并具备拓展至其他科学领域的应用潜力。