{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23141",
  "author_total": 8,
  "authors": [
    {
      "name": "Daniel Gaytan-Villarreal"
    },
    {
      "name": "Aiken Xie"
    },
    {
      "name": "Tu Pham"
    },
    {
      "name": "Rohit Sonker"
    },
    {
      "name": "Chiara Amendola"
    },
    {
      "name": "Matteo Cremonesi"
    },
    {
      "name": "Cong Hao"
    },
    {
      "name": "Jeff Schneider"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0mc3dxhwybs3f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23141",
  "fulltext_url": null,
  "id": "0mc3dxhwybs3f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23141",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "1708|Embedded Systems Design Techniques"
    ]
  ],
  "openalex": "W7214001874",
  "pdf_url": "https://arxiv.org/pdf/2609.23141",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0mc3dxhwybs3f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.23141",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23141"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks",
  "title_text": "Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/0mc3dxhwybs3f",
  "venue": "arXiv",
  "versions": []
}