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Active feedback control in magnetic confinement fusion devices is desirableto mitigate plasma instabilities and enable robust operation. Opticalhigh-speed cameras provide a powerful, non-invasive diagnostic and can besuitable for these applications. In this study, we process fast camera data, atrates exceeding 100kfps, on $\textit{in situ}$ Field Programmable Gate Array(FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generatecontrol signals in real-time. Our system utilizes a convolutional neuralnetwork (CNN) model which predicts the $n$=1 MHD mode amplitude and phase usingcamera images with better accuracy than other tested non-deep-learning-basedmethods. By implementing this model directly within the standard FPGA readouthardware of the high-speed camera diagnostic, our mode tracking system achievesa total trigger-to-output latency of 17.6$\mu$s and a throughput of up to120kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experimentdemonstrates an FPGA-based high-speed camera data acquisition and processingsystem, enabling application in real-time machine-learning-based tokamakdiagnostic and control as well as potential applications in other scientificdomains.

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak