Jetson AGX Xavier vs. Jetson TX2 TensorRT

NVIDIA Jetson TensorRT inference benchmarks by Michael Larabel for a future article on Phoronix.

HTML result view exported from: https://openbenchmarking.org/result/1812240-SP-XAVIER80657&obr_sor=y&obr_rro=y.

Jetson AGX Xavier vs. Jetson TX2 TensorRTProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionJetson AGX XavierJetson TX2ARMv8 rev 0 @ 2.27GHz (8 Cores)jetson-xavier16384MB31GB HBG4a2NVIDIA Tegra XavierASUS VP28UUbuntu 18.044.9.108-tegra (aarch64)Unity 7.5.0X Server 1.19.6NVIDIA 31.0.24.6.01.1.76GCC 7.3.0 + CUDA 10.0ext41920x1080ARMv8 rev 3 @ 2.04GHz (4 Cores / 6 Threads)quill8192MB31GB 032G34NVIDIA Tegra X2VE228Ubuntu 16.044.4.38-tegra (aarch64)Unity 7.4.0X Server 1.18.4NVIDIA 28.2.14.5.0GCC 5.4.0 20160609 + CUDA 9.0OpenBenchmarking.orgProcessor Details- Scaling Governor: tegra_cpufreq schedutil

Jetson AGX Xavier vs. Jetson TX2 TensorRTglmark2: 1920 x 1080tensorrt-inference: VGG16 - FP16 - 4tensorrt-inference: VGG16 - FP16 - 8tensorrt-inference: VGG16 - INT8 - 4tensorrt-inference: VGG16 - INT8 - 8tensorrt-inference: VGG19 - FP16 - 4tensorrt-inference: VGG19 - FP16 - 8tensorrt-inference: VGG19 - INT8 - 4tensorrt-inference: VGG19 - INT8 - 8tensorrt-inference: VGG16 - FP16 - 16tensorrt-inference: VGG16 - FP16 - 32tensorrt-inference: VGG16 - INT8 - 16tensorrt-inference: VGG16 - INT8 - 32tensorrt-inference: VGG19 - FP16 - 16tensorrt-inference: VGG19 - FP16 - 32tensorrt-inference: VGG19 - INT8 - 16tensorrt-inference: VGG19 - INT8 - 32tensorrt-inference: AlexNet - FP16 - 4tensorrt-inference: AlexNet - FP16 - 8tensorrt-inference: AlexNet - INT8 - 4tensorrt-inference: AlexNet - INT8 - 8tensorrt-inference: AlexNet - FP16 - 16tensorrt-inference: AlexNet - FP16 - 32tensorrt-inference: AlexNet - INT8 - 16tensorrt-inference: AlexNet - INT8 - 32tensorrt-inference: ResNet50 - FP16 - 4tensorrt-inference: ResNet50 - FP16 - 8tensorrt-inference: ResNet50 - INT8 - 4tensorrt-inference: ResNet50 - INT8 - 8tensorrt-inference: GoogleNet - FP16 - 4tensorrt-inference: GoogleNet - FP16 - 8tensorrt-inference: GoogleNet - INT8 - 4tensorrt-inference: GoogleNet - INT8 - 8tensorrt-inference: ResNet152 - FP16 - 4tensorrt-inference: ResNet152 - FP16 - 8tensorrt-inference: ResNet152 - INT8 - 4tensorrt-inference: ResNet152 - INT8 - 8tensorrt-inference: ResNet50 - FP16 - 16tensorrt-inference: ResNet50 - FP16 - 32tensorrt-inference: ResNet50 - INT8 - 16tensorrt-inference: ResNet50 - INT8 - 32tensorrt-inference: GoogleNet - FP16 - 16tensorrt-inference: GoogleNet - FP16 - 32tensorrt-inference: GoogleNet - INT8 - 16tensorrt-inference: GoogleNet - INT8 - 32tensorrt-inference: ResNet152 - FP16 - 16tensorrt-inference: ResNet152 - FP16 - 32tensorrt-inference: ResNet152 - INT8 - 16tensorrt-inference: ResNet152 - INT8 - 32Jetson AGX XavierJetson TX22861195.45215.68286.64341.20172.15184.43262.17296.94228.75246.76381.33449.96180.03201.53362.08390.57799124797512371435190018792666542.80582.36865.46944.465468636521049219.08234.84350.28407.015936131106.131184.5085895613401622224.60253.34445.22485.2232.3033.6817.9819.8926.4927.0214.6216.0236.4437.2420.5019.8728.9729.5716.3815.9926130017922237047225830793.6199.0550.3951.0720219811411735.6036.7117.9719.4810611057.1859.4521823012513040.1941.8720.7722.05OpenBenchmarking.org

GLmark2

Resolution: 1920 x 1080

OpenBenchmarking.orgScore, More Is BetterGLmark2Resolution: 1920 x 1080Jetson AGX Xavier60012001800240030002861

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier4080120160200SE +/- 0.30, N = 3SE +/- 3.17, N = 1232.30195.45

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.24, N = 3SE +/- 3.36, N = 533.68215.68

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier60120180240300SE +/- 0.06, N = 3SE +/- 3.98, N = 317.98286.64

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier70140210280350SE +/- 0.05, N = 3SE +/- 1.08, N = 319.89341.20

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier4080120160200SE +/- 0.15, N = 3SE +/- 1.25, N = 326.49172.15

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier4080120160200SE +/- 0.14, N = 3SE +/- 2.36, N = 327.02184.43

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier60120180240300SE +/- 0.10, N = 3SE +/- 0.96, N = 314.62262.17

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier60120180240300SE +/- 0.06, N = 3SE +/- 1.42, N = 316.02296.94

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.11, N = 3SE +/- 1.63, N = 336.44228.75

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.14, N = 3SE +/- 0.17, N = 337.24246.76

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier80160240320400SE +/- 0.03, N = 3SE +/- 10.09, N = 1220.50381.33

NVIDIA TensorRT Inference

Neural Network: VGG16 - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier100200300400500SE +/- 0.03, N = 3SE +/- 4.97, N = 1019.87449.96

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier4080120160200SE +/- 0.11, N = 3SE +/- 11.67, N = 1028.97180.03

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier4080120160200SE +/- 0.09, N = 3SE +/- 1.68, N = 329.57201.53

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier80160240320400SE +/- 0.02, N = 3SE +/- 0.66, N = 316.38362.08

NVIDIA TensorRT Inference

Neural Network: VGG19 - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier80160240320400SE +/- 0.03, N = 3SE +/- 1.67, N = 315.99390.57

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 5.89, N = 12SE +/- 97.79, N = 9261799

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 7.60, N = 12SE +/- 45.66, N = 123001247

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 2.69, N = 4SE +/- 55.83, N = 12179975

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 3.23, N = 3SE +/- 99.61, N = 122221237

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 6.40, N = 12SE +/- 89.56, N = 93701435

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier400800120016002000SE +/- 6.74, N = 3SE +/- 23.33, N = 34721900

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier400800120016002000SE +/- 3.45, N = 3SE +/- 91.41, N = 122581879

NVIDIA TensorRT Inference

Neural Network: AlexNet - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier6001200180024003000SE +/- 0.88, N = 3SE +/- 248.85, N = 93072666

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier120240360480600SE +/- 1.46, N = 3SE +/- 0.39, N = 393.61542.80

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier130260390520650SE +/- 1.23, N = 3SE +/- 0.24, N = 399.05582.36

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 0.64, N = 3SE +/- 14.20, N = 350.39865.46

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 0.54, N = 3SE +/- 40.28, N = 1251.07944.46

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier120240360480600SE +/- 0.88, N = 3SE +/- 96.56, N = 9202546

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 3.70, N = 3SE +/- 14.25, N = 12198863

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier140280420560700SE +/- 2.00, N = 3SE +/- 140.60, N = 12114652

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 2.12, N = 3SE +/- 121.56, N = 101171049

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: FP16 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 4Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.44, N = 3SE +/- 3.18, N = 335.60219.08

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: FP16 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 8Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.67, N = 9SE +/- 0.36, N = 336.71234.84

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: INT8 - Batch Size: 4

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 4Jetson TX2Jetson AGX Xavier80160240320400SE +/- 0.19, N = 3SE +/- 5.48, N = 317.97350.28

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: INT8 - Batch Size: 8

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 8Jetson TX2Jetson AGX Xavier90180270360450SE +/- 0.27, N = 3SE +/- 6.98, N = 319.48407.01

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier130260390520650SE +/- 0.59, N = 3SE +/- 7.03, N = 3106593

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier130260390520650SE +/- 1.29, N = 3SE +/- 9.12, N = 3110613

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 0.10, N = 3SE +/- 11.53, N = 1257.181106.13

NVIDIA TensorRT Inference

Neural Network: ResNet50 - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 0.19, N = 3SE +/- 6.54, N = 359.451184.50

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 3.60, N = 3SE +/- 55.00, N = 9218858

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier2004006008001000SE +/- 3.59, N = 3SE +/- 14.46, N = 12230956

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 1.16, N = 3SE +/- 152.29, N = 91251340

NVIDIA TensorRT Inference

Neural Network: GoogleNet - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier30060090012001500SE +/- 0.91, N = 3SE +/- 5.04, N = 31301622

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: FP16 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 16Jetson TX2Jetson AGX Xavier50100150200250SE +/- 0.17, N = 3SE +/- 15.50, N = 940.19224.60

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: FP16 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 32Jetson TX2Jetson AGX Xavier60120180240300SE +/- 0.14, N = 3SE +/- 2.84, N = 341.87253.34

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: INT8 - Batch Size: 16

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 16Jetson TX2Jetson AGX Xavier100200300400500SE +/- 0.09, N = 3SE +/- 4.04, N = 320.77445.22

NVIDIA TensorRT Inference

Neural Network: ResNet152 - Precision: INT8 - Batch Size: 32

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 32Jetson TX2Jetson AGX Xavier110220330440550SE +/- 0.03, N = 3SE +/- 1.47, N = 322.05485.22


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