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

2026-09-19

AI Summary

本文通过FPGA加速量化循环概率神经网络，解决了托卡马克装置等离子体状态实时预测的低延迟要求问题。

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

Problem

• real-time plasma state estimation
• Tokamak devices
• latency requirements
• plasma control system

Innovation

• FPGA
• Quantized Recurrent Probabilistic Neural Networks
• Real-time Plasma State Prediction
• QKeras
• hls4ml