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resnet18.md

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Benchmark Result for Resnet-18

Environment:

  • GPU: GeForce GTX 1060
  • The number of trials: 128
Op name Pyvlova Running Time (us) TOPI Running Time (us) Pyvlova Tuning Time (s) TOPI Tuning Time (s)
Total 9687.857 4063.218 6705.840 8977.700
resnet18.conv1.pad 9.243 7.230 75.680 -
resnet18.conv1.conv 593.283 170.263 84.370 500.530
resnet18.conv1.bias_layer 59.774 42.433 76.930 -
resnet18.relu1 43.619 41.694 76.380 -
resnet18.maxpool.pad 48.045 44.185 81.000 -
resnet18.maxpool.pool 28.794 29.047 76.150 -
resnet18.layer1.0.conv1.pad 13.354 10.861 78.540 -
resnet18.layer1.0.conv1.conv 452.076 88.885 82.460 519.530
resnet18.layer1.0.conv1.bias_layer 11.764 7.733 76.400 -
resnet18.layer1.0.relu1 9.573 6.759 75.830 -
resnet18.layer1.0.conv2.pad 13.318 10.999 77.850 -
resnet18.layer1.0.conv2.conv 437.529 116.352 81.770 550.640
resnet18.layer1.0.conv2.bias_layer 13.197 7.665 74.850 -
resnet18.layer1.0.eltwise_add 17.197 16.681 75.330 -
resnet18.layer1.0.relu2 9.539 6.609 74.820 -
resnet18.layer1.1.conv1.pad 12.903 11.722 78.350 -
resnet18.layer1.1.conv1.conv 414.566 107.174 81.360 533.150
resnet18.layer1.1.conv1.bias_layer 12.508 7.362 76.940 -
resnet18.layer1.1.relu1 8.367 6.767 73.320 -
resnet18.layer1.1.conv2.pad 12.158 11.709 77.500 -
resnet18.layer1.1.conv2.conv 415.624 106.238 82.040 515.490
resnet18.layer1.1.conv2.bias_layer 11.909 7.210 74.600 -
resnet18.layer1.1.eltwise_add 16.836 16.538 76.300 -
resnet18.layer1.1.relu2 8.915 6.740 74.320 -
resnet18.layer2.0.conv1.pad 12.204 11.151 79.390 -
resnet18.layer2.0.conv1.conv 261.625 117.496 80.240 505.290
resnet18.layer2.0.conv1.bias_layer 5.884 3.067 75.670 -
resnet18.layer2.0.relu1 3.420 2.587 74.350 -
resnet18.layer2.0.conv2.pad 5.587 4.582 79.230 -
resnet18.layer2.0.conv2.conv 453.010 207.162 80.260 542.740
resnet18.layer2.0.conv2.bias_layer 6.708 3.070 74.880 -
resnet18.layer2.downsample.conv 27.711 16.665 80.140 491.940
resnet18.layer2.downsample.bias_layer 5.315 3.059 75.210 -
resnet18.layer2.0.eltwise_add 4.536 2.927 75.520 -
resnet18.layer2.0.relu2 3.615 2.597 74.450 -
resnet18.layer2.1.conv1.pad 5.451 4.587 79.260 -
resnet18.layer2.1.conv1.conv 461.919 137.789 81.230 491.230
resnet18.layer2.1.conv1.bias_layer 4.915 3.065 76.430 -
resnet18.layer2.1.relu1 5.030 2.589 85.560 -
resnet18.layer2.1.conv2.pad 5.797 4.594 92.370 -
resnet18.layer2.1.conv2.conv 463.948 169.947 96.300 538.020
resnet18.layer2.1.conv2.bias_layer 5.509 3.056 87.100 -
resnet18.layer2.1.eltwise_add 3.343 2.945 74.620 -
resnet18.layer2.1.relu2 3.620 2.611 78.730 -
resnet18.layer3.0.conv1.pad 5.785 4.589 78.750 -
resnet18.layer3.0.conv1.conv 282.390 126.730 79.290 443.060
resnet18.layer3.0.conv1.bias_layer 4.049 2.003 74.200 -
resnet18.layer3.0.relu1 2.603 1.879 73.630 -
resnet18.layer3.0.conv2.pad 4.048 2.669 78.590 -
resnet18.layer3.0.conv2.conv 523.266 212.967 80.840 484.640
resnet18.layer3.0.conv2.bias_layer 3.800 2.022 74.410 -
resnet18.layer3.downsample.conv 28.270 22.015 78.510 423.850
resnet18.layer3.downsample.bias_layer 3.303 2.014 74.170 -
resnet18.layer3.0.eltwise_add 2.993 2.099 74.500 -
resnet18.layer3.0.relu2 2.478 1.884 73.270 -
resnet18.layer3.1.conv1.pad 3.165 2.646 77.450 -
resnet18.layer3.1.conv1.conv 504.147 167.278 80.080 464.010
resnet18.layer3.1.conv1.bias_layer 4.212 2.087 74.620 -
resnet18.layer3.1.relu1 2.809 1.887 73.600 -
resnet18.layer3.1.conv2.pad 4.225 2.661 77.570 -
resnet18.layer3.1.conv2.conv 570.701 242.767 79.870 487.890
resnet18.layer3.1.conv2.bias_layer 3.757 2.089 74.660 -
resnet18.layer3.1.eltwise_add 2.888 2.101 73.940 -
resnet18.layer3.1.relu2 2.868 1.958 73.800 -
resnet18.layer4.0.conv1.pad 2.220 2.650 77.430 -
resnet18.layer4.0.conv1.conv 480.366 256.125 78.220 343.600
resnet18.layer4.0.conv1.bias_layer 2.718 1.821 73.080 -
resnet18.layer4.0.relu1 2.285 1.734 72.220 -
resnet18.layer4.0.conv2.pad 2.509 2.439 76.280 -
resnet18.layer4.0.conv2.conv 1062.592 312.967 77.830 335.320
resnet18.layer4.0.conv2.bias_layer 2.931 1.683 73.160 -
resnet18.layer4.downsample.conv 34.528 35.877 77.520 219.990
resnet18.layer4.downsample.bias_layer 2.589 1.685 72.950 -
resnet18.layer4.0.eltwise_add 1.677 1.763 73.020 -
resnet18.layer4.0.relu2 1.557 1.572 72.340 -
resnet18.layer4.1.conv1.pad 3.717 2.427 77.100 -
resnet18.layer4.1.conv1.conv 949.309 477.261 79.070 290.580
resnet18.layer4.1.conv1.bias_layer 3.128 1.685 73.490 -
resnet18.layer4.1.relu1 2.141 1.584 73.080 -
resnet18.layer4.1.conv2.pad 2.534 2.428 76.340 -
resnet18.layer4.1.conv2.conv 713.150 526.853 78.740 289.240
resnet18.layer4.1.conv2.bias_layer 2.580 1.690 73.150 -
resnet18.layer4.1.eltwise_add 1.644 1.773 72.870 -
resnet18.layer4.1.relu2 1.926 1.585 72.720 -
resnet18.avgpool.pool 2.088 7.583 72.060 -
resnet18.flatten 1.472 1.492 68.390 -
resnet18.linear.linear 23.205 15.788 75.000 6.960