mlsanityData to 14 Oct 2026

Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks

arXiv 2609.23141 ↗First posted 19 Sep 2026physics.plasm-ph cs.LGcited by 0

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Abstract (arXiv)

Real time plasma state estimation for control of Tokamak devices are challenging due to the stringent latency requirements of the plasma control system (PCS). We present an end-to-end workflow for deploying a recurrent probabilistic neural network (RPNN) on FPGA hardware. We combine architecture size reduction with quantization-aware training via QKeras. The model is then synthesized using hls4ml, targeting a Xilinx Alveo U50 device. We report a design that fits comfortably within all four resource budgets (DSP, LUT, FF, BRAM) at deterministic sub-10~μs single-timestep latency, meeting the requirements for real-time inference inside a model-predictive-control-style plasma control loop.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 19 Sep 2026.

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11 of 15 references.

References · 11

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  4. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  5. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  6. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  7. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  8. 2022Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4mlcited by 14
  9. 2024Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networkscited by 9
  10. 2026Offline Reinforcement Learning for Rotation Profile Control in Tokamakscited by 2
  11. 2026Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Networkcited by 1

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