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

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 and Gerald A Navratil
arXiv.org
30 Nov 2023
DOI: https://doi.org/10.48550/arxiv.2312.00128
url
https://doi.org/10.48550/arXiv.2312.00128View
Preprint (Author's original) Open arXiv.org - Non-exclusive license to distribute

Abstract

Computer Science - Hardware Architecture Computer Science - Learning Physics - Instrumentation and Detectors Physics - Plasma Physics
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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