{
  "abstract": "We present a real-time field-programmable gate array (FPGA) implementation of a convolutional neural network (CNN) for event classification on full calorimeter images that retain crystal-level shower information. To meet the resource and latency constraints of trigger electronics, the network parameters and hidden activations were quantized to 8 bits with quantization-aware training, and a lossless space-to-depth reorganization rearranged the input image into fewer spatial positions, reducing the estimated CNN inference latency by more than an order of magnitude. The approach was evaluated with the J-PARC KOTO detector, recorded data, and data-acquisition (DAQ) constraints as a benchmark, targeting the online separation of the rare $K^{0}_{L}\\rightarrowπ^{0}e^{+}e^{-}$ decay from its dominant $K^{0}_{L}\\rightarrowπ^{+}π^{-}π^{0}$ background. As a proof of principle, the firmware was implemented and validated on an AMD Versal Premium VPK120 evaluation board. On 100,000 test events, inference on the board rejected $(99.34\\pm0.04)\\%$ of the recorded background sample enriched in $K^{0}_{L}\\rightarrowπ^{+}π^{-}π^{0}$ decays at a nominal $K^{0}_{L}\\rightarrowπ^{0}e^{+}e^{-}$ efficiency of 95%, with a measured CNN inference latency of 17.9 $μ$s. Combining the measured image-assembly and CNN inference latencies with the estimated link-transfer and score-return times gives a projected end-to-end latency of 25.9 $μ$s, within the adopted 32 $μ$s DAQ latency budget. These results demonstrate the feasibility of using full-granularity calorimeter images for CNN-based event selection that achieves high background rejection while keeping the trigger latency within tens of microseconds.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36720",
  "author_total": 5,
  "authors": [
    {
      "name": "Jia-Jen Wu"
    },
    {
      "name": "Yun-Tsung Lai"
    },
    {
      "name": "Yu-Chen Tung"
    },
    {
      "name": "Sin-Ting Chen"
    },
    {
      "name": "Yu-Sheng Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/662gz84a978pc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36720",
  "fulltext_url": null,
  "id": "662gz84a978pc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36720",
  "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",
      "hep-ex"
    ],
    [
      "arxiv",
      "physics.ins-det"
    ],
    [
      "openalex",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7224101350",
  "pdf_url": "https://arxiv.org/pdf/2610.36720",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/662gz84a978pc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36720",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36720"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Development of a real-time FPGA-based CNN trigger operating on full-granularity calorimeter images",
  "title_text": "Development of a real-time FPGA-based CNN trigger operating on full-granularity calorimeter images",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/662gz84a978pc",
  "venue": "arXiv",
  "versions": []
}