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Area of Science:

  • Plasma physics and fusion energy research.
  • Advanced diagnostics and real-time control systems.
  • Machine learning applications in scientific instrumentation.

Background:

  • Active feedback control is crucial for stabilizing plasma instabilities in magnetic confinement fusion devices.
  • High-speed optical cameras offer non-invasive plasma monitoring capabilities.
  • Real-time processing of diagnostic data is essential for effective control.

Purpose of the Study:

  • To develop and demonstrate an in situ FPGA-based system for processing high-speed camera data.
  • To track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time.
  • To leverage deep learning for enhanced accuracy in plasma mode prediction.

Main Methods:

  • Utilized high-speed cameras operating at over 100 kfps.
  • Implemented a convolutional neural network (CNN) model on in situ field-programmable gate array (FPGA) hardware.
  • Integrated the CNN model within the camera's FPGA readout for low-latency processing.

Main Results:

  • Achieved real-time tracking of magnetohydrodynamic (MHD) mode amplitude and phase.
  • CNN model demonstrated superior accuracy in predicting MHD modes compared to non-deep-learning methods.
  • System achieved a total trigger-to-output latency of 17.6 μs and a throughput of 120 kfps.

Conclusions:

  • The FPGA-based high-speed camera system enables real-time, machine-learning-driven tokamak diagnostics and control.
  • Demonstrated the feasibility of integrating advanced AI models into fusion diagnostic hardware.
  • The system holds potential for broader applications in scientific domains requiring high-speed data processing and analysis.