Low latency optical-based mode tracking with machine learning deployed
  on FPGAs on a tokamak
Yumou Wei Ryan F. Forelli Chris Hansen Jeffrey P. Levesque Nhan Tran Joshua C. Agar Giuseppe Di Guglielmo Michael E. Mauel Gerald A. Navratil
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.