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Journal article
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Review of scientific instruments, v 95(7)
01 Jul 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 high-speed camera data, at rates exceeding 100 kfps, on 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 μs and a throughput of up to 120 kfps. 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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Details
- Title
- Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
- Creators
- Y. Wei - Columbia UniversityR. F. Forelli - Lehigh UniversityC. Hansen - Columbia UniversityJ. P. Levesque - Columbia UniversityN. Tran - Fermi National Accelerator LaboratoryJ. C. Agar - Drexel UniversityG. Di Guglielmo - Fermi National Accelerator LaboratoryM. E. Mauel - Columbia UniversityG. A. Navratil - Columbia University
- Publication Details
- Review of scientific instruments, v 95(7)
- Publisher
- American Institute of Physics (AIP)
- Number of pages
- 12
- Grant note
- Real-time Data Reduction Codesign at the Extreme Edge for Science (DE-FOA-0002501) / Advanced Scientific Computing Research (https://doi.org/10.13039/100006192) 2320600; 2215789 / National Science Foundation Major Research Instrumentation Program DE-SC0022234; DE-SC0021325; DE-FG02-86ER53222 / Fusion Energy Sciences (https://doi.org/10.13039/100006207) DE-AC02-07CH11359 / High Energy Physics (https://doi.org/110.13039/100006208)
- Resource Type
- Journal article
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:001265597400003
- Scopus ID
- 2-s2.0-85198318524
- Other Identifier
- 991021893215204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Instruments & Instrumentation
- Physics, Applied