definitions/simd-loop/loop_114.json

{
  "name": "loop_114",
  "op_type": "loop_114",
  "description": "Compute auto-correlation of an integer array with widening accumulation",
  "tags": [
    "simd-loop",
    "sme2-kernel:streaming-sve"
  ],
  "axes": {
    "n": {
      "type": "var",
      "description": "axis n"
    },
    "lags": {
      "type": "var",
      "description": "axis lags"
    },
    "scale": {
      "type": "const",
      "value": 4,
      "description": "const scale"
    }
  },
  "inputs": {
    "data": {
      "shape": [
        "n"
      ],
      "dtype": "int16"
    }
  },
  "outputs": {
    "res": {
      "shape": [
        "lags"
      ],
      "dtype": "int16",
      "description": "Output array"
    }
  },
  "reference": "import numpy as np\n\ndef run(data):\n    n = int(data.shape[0])\n    scale = 4\n    d = data.astype(np.int64)\n    res = np.zeros(n, dtype=np.int16)\n    for lag in range(n):\n        # Same arithmetic as the scalar C reference: a wrapping int32\n        # accumulator, arithmetic shift, then a wrapping int16 store.\n        acc = int(((d[:n - lag] * d[lag:]) >> scale).sum())\n        acc = ((acc + 2**31) % 2**32) - 2**31\n        v = acc >> 16\n        res[lag] = ((v + 2**15) % 2**16) - 2**15\n    return res\n",
  "simd_loop_meta": {
    "output_inplace": false,
    "array_pad": 0,
    "scratch": [],
    "axes_order": [
      "n",
      "lags",
      "scale"
    ]
  }
}