{
  "annotation_id": "3549f584-4d80-4c98-99a8-f211fe7f6a53",
  "date_created": "2026-03-02T18:01:17.763000Z",
  "date_modified": "2026-03-02T18:01:17.763000Z",
  "file_hash": "ab3245a10d3da8096ce3e2efc3c951414e5b1459286331813e4fb349ce5d166b",
  "private": false,
  "record": {
    "abstract": "An artificial neural network algorithm is implemented using a field\nprogrammable gate array hardware. One hidden layer is used in the feed-forward\nneural network structure in order to discriminate one class of patterns from\nthe other class in real time. With five 8-bit input patterns, six hidden nodes,\nand one 8-bit output, the implemented hardware neural network makes decision on\na set of input patterns in 11 clocks and the result is identical to what to\nexpect from off-line computation. This implementation may be used in level 1\nhardware triggers in high energy physics experiments",
    "arxiv_id": "physics/0703041",
    "authors": [
      "E. Won"
    ],
    "categories": [
      "physics.ins-det"
    ],
    "doi": "10.1016/j.nima.2007.08.163",
    "title": "A Hardware Implementation of Artificial Neural Network Using Field   Programmable Gate Arrays",
    "url": "https://arxiv.org/abs/physics/0703041"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d05824b9-1602-4ad6-b089-9f50077b1fce",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}