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

• URL: http://arxiv.org/abs/2312.00128v3
• Date: Tue, 9 Jul 2024 16:20:06 GMT
• Title: Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
• Authors: Yumou Wei, Ryan F. Forelli, Chris Hansen, Jeffrey P. Levesque, Nhan Tran, Joshua C. Agar, Giuseppe Di Guglielmo, Michael E. Mauel, Gerald A. Navratil,
• Abstract summary: This study demonstrates an FPGA-based high-speed camera data acquisition and processing system. It enables application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
• Score: 0.8506991993461593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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