)]}'
{
  "commit": "fb4f8d2dd93ef8bd8fb6c16d508e6e006c516c36",
  "tree": "a07a770a16917debe76c4d689be2d0b29e1e35b2",
  "parents": [
    "5d75e54d9d71a9627e7519d6bacb6ff9888ac4a5"
  ],
  "author": {
    "name": "Stanley Winata",
    "email": "68087699+raikonenfnu@users.noreply.github.com",
    "time": "Fri Sep 30 16:04:03 2022 -0700"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Fri Sep 30 16:04:03 2022 -0700"
  },
  "message": "[flow] Generalize 1x1 Conv2D to matmul for NCHW (#10616)\n\nMost models from PyTorch uses NCHW Conv2D by default. To accelerate the\r\n1x1 filter Conv2D workload with NCHW layout, we generalize the 1x1 filter\r\nConv2D to matmul pass to be able to handle NCHW as well.\r\n\r\nPerf boost on ResNet50 (PyTorch):\r\n- CPU (Threadripper PRO 3995WX): 100ms -\u003e 80ms\r\n- Vulkan (Threadripper PRO 3995WX): 48ms -\u003e 31.7ms",
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