{
  "name": "loop_038",
  "op_type": "loop_038",
  "description": "1D convolution of an FP16 signal with an FP16 filter kernel",
  "tags": [
    "simd-loop"
  ],
  "axes": {
    "dim": {
      "type": "var",
      "description": "axis dim"
    }
  },
  "inputs": {
    "a": {
      "shape": [
        "dim",
        "dim"
      ],
      "dtype": "float16"
    },
    "b": {
      "shape": [
        "dim",
        "dim"
      ],
      "dtype": "float16"
    }
  },
  "outputs": {
    "c": {
      "shape": [
        "dim",
        "dim"
      ],
      "dtype": "float16",
      "description": "Output array"
    }
  },
  "reference": "import numpy as np\n\ndef run(a, b):\n    dim = a.shape[0]\n    c = np.zeros((dim, dim), dtype=np.float16)\n    if dim >= 2:\n        k = np.float16(0.25)\n        s0 = a[:-1, :-1]; s1 = a[:-1, 1:]; s2 = a[1:, :-1]; s3 = a[1:, 1:]\n        r = (b[:-1, :-1] + s0 * k).astype(np.float16)\n        r = (r + s1 * k).astype(np.float16)\n        r = (r + s2 * k).astype(np.float16)\n        r = (r + s3 * k).astype(np.float16)\n        c[:-1, :-1] = r\n    return c\n",
  "simd_loop_meta": {
    "output_inplace": false,
    "array_pad": 0,
    "scratch": [],
    "axes_order": [
      "dim"
    ]
  }
}