ODROID-N2 Benchmark Comparison

ODROID-N2 benchmarks for a future article.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 1904251-HV-ODROIDN2760
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C/C++ Compiler Tests 4 Tests
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Programmer / Developer System Benchmarks 2 Tests
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Identifier
Performance Per
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Date
Run
  Test
  Duration
Jetson TX1 Max-P
March 17 2019
  1 Hour, 20 Minutes
Jetson TX2 Max-Q
March 16 2019
  7 Hours, 23 Minutes
Jetson TX2 Max-P
March 15 2019
  6 Hours, 25 Minutes
Jetson AGX Xavier
March 15 2019
  4 Hours, 1 Minute
Jetson Nano
March 17 2019
  7 Hours, 18 Minutes
Raspberry Pi 3 Model B+
March 16 2019
  4 Hours, 32 Minutes
ASUS TinkerBoard
March 16 2019
  7 Hours, 20 Minutes
ODROID-XU4
March 17 2019
  4 Hours, 21 Minutes
ODROID-N2
April 21 2019
  1 Hour, 59 Minutes
ODROID-C2
April 24 2019
  8 Hours, 20 Minutes
Invert Hiding All Results Option
  5 Hours, 18 Minutes

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ODROID-N2 Benchmark Comparison, "CUDA Mini-Nbody 2015-11-10 - Performance / Cost - Test: Original", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "CUDA Mini-Nbody 2015-11-10 - Test: Original", Higher Results Are Better "Jetson AGX Xavier",47.134,47.124,47.135 "Jetson TX2 Max-P",8.24,8.227,8.248 "Jetson TX2 Max-Q",6.708,6.802,6.796 "Jetson Nano",4.067,4.066,4.085 "TTSIOD 3D Renderer 2.3b - Performance / Cost - Phong Rendering With Soft-Shadow Mapping", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "TTSIOD 3D Renderer 2.3b - Phong Rendering With Soft-Shadow Mapping", Higher Results Are Better "Jetson AGX Xavier",150.978,134.802,131.337,132.463,132.031,130.64,130.869,131.891,131.491,130.971,130.832,132.078 "Jetson TX2 Max-P",49.3978,49.4203,48.9575 "Jetson TX2 Max-Q",29.3872,29.3644,27.4862,29.1488 "Raspberry Pi 3 Model B+",17.984,17.5028,17.501 "ASUS TinkerBoard",23.3451,21.2351,20.9586,20.8803,20.8479,20.8715,20.9866,20.9135,20.9058 "Jetson TX1 Max-P",45.0771,45.163,45.0377 "ODROID-XU4",49.1282,42.8975,39.7692,40.4817,41.5918,42.5092,40.0077,41.0873,40.1642 "Jetson Nano",40.9025,40.7681,41.1524 "ODROID-N2",57.3582,57.3717,57.5228 "ODROID-C2",21.9328,22.2063,22.1459 "NVIDIA TensorRT Inference - Neural Network: VGG16 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",208.703,208.962,208.628 "Jetson TX2 Max-P",32.143,31.6987,33.981,32.7477 "Jetson TX2 Max-Q",26.1732,26.0698,25.7346 "Jetson Nano",14.3692,14.3311 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Neural Network: VGG16 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",303.711,303.005,304.611 "Jetson TX2 Max-P",16.3612,17.7045,17.707,17.77,17.727,18.1063 "Jetson TX2 Max-Q",14.3354,14.5968,13.4569,14.3976,14.3904 "NVIDIA TensorRT Inference - Neural Network: VGG19 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",394.465,395.111,394.399 "Jetson TX2 Max-P",15.9486,16.0019,15.8117 "Jetson TX2 Max-Q",12.5342,12.6257,12.6031 "NVIDIA TensorRT Inference - Neural Network: AlexNet - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1201.46,1201.91,1196.24 "Jetson TX2 Max-P",278.36,222.341,249.778,255.787,277.473,304.014,257.594,228.296,294.245,245.232,299.846,253.799 "Jetson TX2 Max-Q",202.585,224.229,215.21,217.911,218.577,220.182 "Jetson Nano",120.801,109.249,113.019,127.583,125.389,114.899,113.584,120.422,118.392,125.583,126.446,104.749 "NVIDIA TensorRT Inference - Neural Network: AlexNet - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1147.08,1138.12,1142.92 "Jetson TX2 Max-P",180.902,175.708,192.253,184.533,187.075 "Jetson TX2 Max-Q",148.1,149.833,146.674 "Jetson Nano",82.8754,84.0409,85.382 "NVIDIA TensorRT Inference - Neural Network: AlexNet - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",2037.23,2042.31,2035.4 "Jetson TX2 Max-P",418.289,485.379,493.491,432.306,488.111,441.301,465.489,479.41,435.698,473.94,440.839,489.368 "Jetson TX2 Max-Q",377.966,368.297,374.434 "Jetson Nano",202.539,202.319,197.659 "NVIDIA TensorRT Inference - Neural Network: AlexNet - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",3142.28,3144.59,3140.95 "Jetson TX2 Max-P",301.864,300.93,300.061 "Jetson TX2 Max-Q",235.623,235.707,239.839 "Jetson Nano",127.62,127.472,127.67 "NVIDIA TensorRT Inference - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",547.485,547.458,547.56 "Jetson TX2 Max-P",94.1442,86.2995,95.9709,91.9219,95.8238,89.0835,89.939,89.334,85.3654,91.5064,100.018,97.9283 "Jetson TX2 Max-Q",77.5964,74.5251,72.0198,66.272,69.865,75.839,74.3196,70.7943,69.4287,73.1548,65.2801,74.9964 "Jetson Nano",40.8261,41.5429,40.749 "NVIDIA TensorRT Inference - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",902.395,899.762,906.179 "Jetson TX2 Max-P",47.9151,50.0049,51.7866,50.166 "Jetson TX2 Max-Q",39.9034,37.8756,39.6696 "Jetson Nano",21.5321,20.2945,21.068 "NVIDIA TensorRT Inference - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",797.316,790.769,798.875 "Jetson TX2 Max-P",193.491,201.347,197.376 "Jetson TX2 Max-Q",153.162,160.497,165.878,151.554,163.542,148.371,155.992,155.301,148.849,161.288,163.564,146.96 "Jetson Nano",82.7407,84.7738,82.6049 "NVIDIA TensorRT Inference - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1137.32,1151.7,1148.02 "Jetson TX2 Max-P",110.896,112.155,116.361 "Jetson TX2 Max-Q",90.6201,86.2883,89.7201 "Jetson Nano",46.8307,48.891,47.7487 "NVIDIA TensorRT Inference - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",224.335,223.747,224.477 "Jetson TX2 Max-P",35.6056,34.4145,35.3045 "Jetson TX2 Max-Q",27.984,26.8478,27.1896 "Jetson Nano",15.8286,15.7086,15.7311 "NVIDIA TensorRT Inference - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",375.594,372.502,370.096 "Jetson TX2 Max-P",18.4577,18.3856,18.0213 "Jetson TX2 Max-Q",14.7989,14.4269,14.2882 "Jetson Nano",7.80364,7.71499,7.75225 "NVIDIA TensorRT Inference - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",637.765,636.563,633.635 "Jetson TX2 Max-P",110.76,112.798,108.572 "Jetson TX2 Max-Q",84.3897,87.208,86.6317 "Jetson Nano",46.5463,46.4845,46.4974 "NVIDIA TensorRT Inference - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1214.64,1215.5,1215.1 "Jetson TX2 Max-P",59.7418,59.6222,59.7005 "Jetson TX2 Max-Q",47.1592,47.2757,47.0066 "Jetson Nano",25.0589,24.9801,25.1875 "NVIDIA TensorRT Inference - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1005.65,1006.32,1005.76 "Jetson TX2 Max-P",223.961,237.216,237.674 "Jetson TX2 Max-Q",186.265,174.378,185.755,171.571,174.575,174.056,183.977,183.653 "Jetson Nano",99.0927,99.1556,98.5501 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG16 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",1700.28,1675.82,1703.42 "Jetson TX2 Max-P",131.414,128.848,130.083 "Jetson TX2 Max-Q",103.681,103.755,103.513 "Jetson Nano",55.297,55.893,55.7755 "NVIDIA TensorRT Inference - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",260.15,260.002,259.311 "Jetson TX2 Max-P",42.0445,41.8598,41.8123 "Jetson TX2 Max-Q",32.8097,32.4759,32.7174 "Jetson Nano",17.3879,17.3703,17.3957 "NVIDIA TensorRT Inference - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",491.631,493.708,494.307 "Jetson TX2 Max-P",22.1156,22.049,22.031 "Jetson TX2 Max-Q",17.368,17.3589,17.3512 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG16 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG19 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG19 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG16 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG16 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG19 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Neural Network: VGG16 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",248.139,247.726,247.981 "Jetson TX2 Max-P",37.1824,36.2465,37.1919 "Jetson TX2 Max-Q",29.4819,29.8775,30.1157 "NVIDIA TensorRT Inference - Neural Network: VGG19 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",203.915,203.924,204.032 "Jetson TX2 Max-P",29.7627,29.9376,29.7943 "Jetson TX2 Max-Q",24.037,23.7941,23.9907 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: VGG19 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: AlexNet - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: AlexNet - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: AlexNet - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: AlexNet - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Neural Network: VGG19 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",173.429,171.708,172.356 "Jetson TX2 Max-P",26.5966,27.2027,25.8709 "Jetson TX2 Max-Q",20.8847,21.6977,20.5357 "Jetson Nano",11.6433,11.5434 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Neural Network: VGG19 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",266.113,265.876,265.435 "Jetson TX2 Max-P",13.6304,14.3678,14.8003,14.462 "Jetson TX2 Max-Q",11.6595,11.6931,10.9886 "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 4 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Jetson Nano", "NVIDIA TensorRT Inference - Performance / Cost - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "NVIDIA TensorRT Inference - Neural Network: VGG16 - Precision: INT8 - Batch Size: 32 - DLA Cores: Disabled", Higher Results Are Better "Jetson AGX Xavier",475.195,474.877,475.154 "Jetson TX2 Max-P",19.8313,19.9879,19.9144 "Jetson TX2 Max-Q",15.81,15.7621,15.7914 "7-Zip Compression 16.02 - Performance / Cost - Compress Speed Test", Higher Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "7-Zip Compression 16.02 - Compress Speed Test", Higher Results Are Better "Jetson AGX Xavier",16806,17652,19749,19582,19705,19527,19653,19780,19659,19415,19531,19483 "Jetson TX2 Max-P",5571,5635,5574 "Jetson TX2 Max-Q",3313,3300,3269 "Raspberry Pi 3 Model B+",1778,2056,2047,2030,2041,2029,2031,2009,2039,2037,2042 "ASUS TinkerBoard",2866,2875,2766 "Jetson TX1 Max-P",4529,4512,4483 "ODROID-XU4",4164,3810,4934,4463,4093,4087,3798,3949,4070,4053,3958,4064 "Jetson Nano",4084,4037,4025 "ODROID-N2",5969,5967,5975 "ODROID-C2",2115,2136,2113 "LeelaChessZero 0.20.1 - Backend: BLAS", Higher Results Are Better "Jetson AGX Xavier",51.2966,47.2233,46.4651,47.1557,46.7206,47.1349,47.3134 "Jetson Nano",15.3987,15.3082,15.3961 "ODROID-N2",24.4475,24.1965,24.5247 "ODROID-C2",6.90482,7.31003,7.71017,7.49065,7.30255,7.28799,7.28689 "LeelaChessZero 0.20.1 - Backend: CUDA + cuDNN", Higher Results Are Better "Jetson AGX Xavier",960.648,940.764,957.256 "Jetson Nano",140.308,139.466,139.604 "LeelaChessZero 0.20.1 - Backend: CUDA + cuDNN FP16", Higher Results Are Better "Jetson AGX Xavier",2523.19,2499.83,2522.01 "LeelaChessZero 0.20.1 - Performance / Cost - Backend: BLAS", Higher Results Are Better "Jetson AGX Xavier", "Jetson Nano", "ODROID-N2", "LeelaChessZero 0.20.1 - Performance / Cost - Backend: CUDA + cuDNN", Higher Results Are Better "Jetson AGX Xavier", "Jetson Nano", "LeelaChessZero 0.20.1 - Performance / Cost - Backend: CUDA + cuDNN FP16", Higher Results Are Better "Jetson AGX Xavier", "Meta Performance Per Dollar - Performance Per Dollar", Higher Results Are Better "ODROID-N2", "GLmark2 - Resolution: 1920 x 1080", Higher Results Are Better "Jetson AGX Xavier", "Jetson Nano", "GLmark2 - Performance / Cost - Resolution: 1920 x 1080", Higher Results Are Better "Jetson AGX Xavier", "Jetson Nano", "PyBench 2018-02-16 - Total For Average Test Times", Lower Results Are Better "Jetson AGX Xavier",2998,3008,3014 "Jetson TX2 Max-P",5366,5475,5383 "Jetson TX2 Max-Q",8690,8695,8820 "Raspberry Pi 3 Model B+",20945,20826,20967 "ASUS TinkerBoard",15691,16297,10322,10167,10110,10339,10437,9624,10534 "Jetson TX1 Max-P",6351,6364,6303 "ODROID-XU4",4990,5070,4968 "Jetson Nano",7066,7156,7031 "ODROID-N2",5215,5230,5247 "ODROID-C2",12207,12128,12217 "PyBench 2018-02-16 - Performance / Cost - Total For Average Test Times", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "C-Ray 1.1 - Total Time - 4K, 16 Rays Per Pixel", Lower Results Are Better "Jetson AGX Xavier",300.326,343.662,364.332,364.688,364.144,363.987,364.164,363.96,363.764 "Jetson TX2 Max-P",977.761,533.547,534.592,538.351,536.314,533.971,548.331,531.981,532.463 "Jetson TX2 Max-Q",865.785,870.174,870.017 "Raspberry Pi 3 Model B+",2034.332,2028.928,2025.911 "ASUS TinkerBoard",1673.941,1741.493,1738.825 "Jetson TX1 Max-P",741.179,772.973,743.533 "ODROID-XU4",1020.682,859.796,781.074,807.339,748.376,831.261,893.198,753.826,747.783 "Jetson Nano",921.883,920.709,921.024 "ODROID-N2",491.518,492.268,491.534 "ODROID-C2",1535.126,1534.616,1534.719 "Rust Prime Benchmark - Prime Number Test To 200,000,000", Lower Results Are Better "Jetson AGX Xavier",32.369006156921,32.359191894531,32.367820978165 "Jetson TX2 Max-P",104.92546510696,105.04112505913,104.90127086639 "Jetson TX2 Max-Q",170.26361608505,170.08605980873,170.4050219059 "Raspberry Pi 3 Model B+",1100.7618257999,1095.8513560295,1096.4616448879 "ASUS TinkerBoard",1527.2502539158,1414.4890110493,1407.0128879547,1968.4427921772,2033.5513451099,2575.5730659962 "Jetson TX1 Max-P",128.75703287125,129.60009694099,127.0019800663 "ODROID-XU4",574.84182405472,573.67734313011,573.8012239933 "Jetson Nano",150.4321539402,149.75622010231,150.3797390461 "ODROID-N2",73.15295290947,73.083229064941,73.092663049698 "ODROID-C2",126.3935611248,125.42829012871,125.60522508621 "Zstd Compression 1.3.4 - Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19", Lower Results Are Better "Jetson AGX Xavier",78.261996984482,81.176754951477,80.735954999924 "Jetson TX2 Max-P",144.40146207809,145.3891749382,145.10904502869 "Jetson TX2 Max-Q",252.57122015953,255.82780098915,252.99618387222 "Raspberry Pi 3 Model B+",343.86739301682,342.51502299309,340.31952023506 "ASUS TinkerBoard",493.97650408745,500.89577293396,494.98889088631 "Jetson TX1 Max-P",146.09001684189,144.97263598442,146.3298740387 "Jetson Nano",129.7424120903,130.30371499062,129.55208706856 "ODROID-N2",153.93824005127,148.51493406296,153.6802008152 "ODROID-C2",312.12533116341,316.95134902,313.8998169899 "FLAC Audio Encoding 1.3.2 - WAV To FLAC", Lower Results Are Better "Jetson AGX Xavier",56.730622053146,54.393737077713,53.064939975739,53.975198030472,54.179378032684 "Jetson TX2 Max-P",65.099822998047,65.641438961029,64.799404144287,64.830460071564,64.956172943115 "Jetson TX2 Max-Q",103.99030089378,104.48815512657,104.76083803177,104.37327098846,103.7655339241 "Raspberry Pi 3 Model B+",339.31037282944,337.10595822334,342.56921696663,340.70349907875,337.95009493828 "ASUS TinkerBoard",286.25495100021,281.78744792938,279.70542287827,271.50059986115,275.99261808395 "Jetson TX1 Max-P",80.643102169037,80.375013828278,80.03696680069,76.877398014069,78.043719053268 "ODROID-XU4",97.058123111725,96.899234056473,96.224318027496,96.844074964523,98.121542930603 "Jetson Nano",103.41150212288,105.25729489326,103.14293313026,107.75390219688,104.28678107262 "ODROID-N2",94.94019317627,95.510184049606,95.188143014908,95.880253076553,96.453732967377 "ODROID-C2",265.96660017967,263.98608803749,260.36004304886,263.66789484024,257.57013702393 "OpenCV Benchmark 3.3.0 - ", Lower Results Are Better "Jetson AGX Xavier",125.26104307175,130.6886138916,127.53062081337 "Jetson TX2 Max-P",295.73621296883,295.94987106323,296.61644697189 "Jetson TX2 Max-Q",504.22019696236,486.66019916534,487.34665894508 "Raspberry Pi 3 Model B+",2.7371959686279 "ODROID-XU4",531.30143499374,514.98779511452,515.79874587059 "Jetson Nano",304.63343286514,263.82345294952,281.0886631012,271.52301716805,263.16651701927,264.02024912834,265.15014791489,264.3737039566,261.62518119812 "ODROID-N2",242.97086191177,242.65014100075,243.52494502068 "ODROID-C2",471.06256699562,481.30278301239,470.69004201889 "Tesseract OCR 4.0.0-beta.1 - Time To OCR 7 Images", Lower Results Are Better "Jetson AGX Xavier",73.572254896164,71.729460000992,70.515511989594 "ODROID-XU4",180.56019186974,183.09073805809,178.31624317169 "Jetson Nano",135.4518301487,132.27096486092,130.28921198845 "ODROID-N2",110.8050558567,110.74856591225,110.64985084534 "ODROID-C2",218.98877286911,221.97846794128,220.36140203476 "C-Ray 1.1 - Performance / Cost - Total Time - 4K, 16 Rays Per Pixel", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "Rust Prime Benchmark - Performance / Cost - Prime Number Test To 200,000,000", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "Zstd Compression 1.3.4 - Performance / Cost - Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "Jetson Nano", "ODROID-N2", "FLAC Audio Encoding 1.3.2 - Performance / Cost - WAV To FLAC", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ASUS TinkerBoard", "Jetson TX1 Max-P", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "OpenCV Benchmark 3.3.0 - Performance / Cost - ", Lower Results Are Better "Jetson AGX Xavier", "Jetson TX2 Max-P", "Jetson TX2 Max-Q", "Raspberry Pi 3 Model B+", "ODROID-XU4", "Jetson Nano", "ODROID-N2", "Tesseract OCR 4.0.0-beta.1 - Performance / Cost - Time To OCR 7 Images", Lower Results Are Better "Jetson AGX Xavier", "ODROID-XU4", "Jetson Nano", "ODROID-N2",