File size: 1,401 Bytes
f25167e 144919f f25167e bd25fbe f25167e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"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"
]
}
}
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