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

Abstract: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~$\mu$s single-timestep latency, meeting the requirements for real-time inference inside a model-predictive-control-style plasma control loop.

Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG)

arXiv:2609.23141 [physics.plasm-ph]

(or arXiv:2609.23141v1 [physics.plasm-ph] for this version)

https://doi.org/10.48550/arXiv.2609.23141 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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