new ai AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG CROSSHAIR X670E HERO (1101 BIOS) and AMD Radeon RX 7900 XTX 24GB on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2304292-PTS-NEWAI12536 .
new ai Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server OpenGL Vulkan Compiler File-System Screen Resolution a b c AMD Ryzen 9 7950X 16-Core @ 4.50GHz (16 Cores / 32 Threads) ASUS ROG CROSSHAIR X670E HERO (1101 BIOS) AMD Device 14d8 32GB 2048GB SOLIDIGM SSDPFKKW020X7 + 2000GB AMD Radeon RX 7900 XTX 24GB (2304/1249MHz) AMD Device ab30 ASUS MG28U Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411 Ubuntu 22.04 6.3.0-060300rc7daily20230417-generic (x86_64) GNOME Shell 42.5 X Server 1.21.1.3 + Wayland 4.6 Mesa 23.2.0-devel (git-f6fb189 2023-04-18 jammy-oibaf-ppa) (LLVM 15.0.7 DRM 3.52) 1.3.246 GCC 11.3.0 ext4 3840x2160 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa601203 Python Details - Python 3.10.9 Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
new ai svt-av1: Preset 4 - Bosphorus 4K svt-av1: Preset 8 - Bosphorus 4K svt-av1: Preset 12 - Bosphorus 4K svt-av1: Preset 13 - Bosphorus 4K svt-av1: Preset 4 - Bosphorus 1080p svt-av1: Preset 8 - Bosphorus 1080p svt-av1: Preset 12 - Bosphorus 1080p svt-av1: Preset 13 - Bosphorus 1080p faiss: demo_sift1M faiss: bench_polysemous_sift1m - PQ baseline faiss: bench_polysemous_sift1m - Polysemous 64 faiss: bench_polysemous_sift1m - Polysemous 62 faiss: bench_polysemous_sift1m - Polysemous 58 faiss: bench_polysemous_sift1m - Polysemous 54 faiss: bench_polysemous_sift1m - Polysemous 50 faiss: bench_polysemous_sift1m - Polysemous 46 faiss: bench_polysemous_sift1m - Polysemous 42 faiss: bench_polysemous_sift1m - Polysemous 38 faiss: bench_polysemous_sift1m - Polysemous 34 faiss: bench_polysemous_sift1m - Polysemous 30 intel-tensorflow: resnet50_fp32_pretrained_model - 1 intel-tensorflow: resnet50_fp32_pretrained_model - 1 intel-tensorflow: resnet50_int8_pretrained_model - 1 intel-tensorflow: resnet50_int8_pretrained_model - 1 intel-tensorflow: resnet50_fp32_pretrained_model - 16 intel-tensorflow: resnet50_fp32_pretrained_model - 32 intel-tensorflow: resnet50_fp32_pretrained_model - 64 intel-tensorflow: resnet50_fp32_pretrained_model - 96 intel-tensorflow: resnet50_int8_pretrained_model - 16 intel-tensorflow: resnet50_int8_pretrained_model - 32 intel-tensorflow: resnet50_int8_pretrained_model - 64 intel-tensorflow: resnet50_int8_pretrained_model - 96 intel-tensorflow: resnet50_fp32_pretrained_model - 256 intel-tensorflow: resnet50_fp32_pretrained_model - 512 intel-tensorflow: resnet50_int8_pretrained_model - 256 intel-tensorflow: resnet50_int8_pretrained_model - 512 intel-tensorflow: inceptionv4_fp32_pretrained_model - 1 intel-tensorflow: inceptionv4_fp32_pretrained_model - 1 intel-tensorflow: inceptionv4_int8_pretrained_model - 1 intel-tensorflow: inceptionv4_int8_pretrained_model - 1 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 1 intel-tensorflow: mobilenetv1_int8_pretrained_model - 1 intel-tensorflow: inceptionv4_fp32_pretrained_model - 16 intel-tensorflow: inceptionv4_fp32_pretrained_model - 32 intel-tensorflow: inceptionv4_fp32_pretrained_model - 64 intel-tensorflow: inceptionv4_fp32_pretrained_model - 96 intel-tensorflow: inceptionv4_int8_pretrained_model - 16 intel-tensorflow: inceptionv4_int8_pretrained_model - 32 intel-tensorflow: inceptionv4_int8_pretrained_model - 64 intel-tensorflow: inceptionv4_int8_pretrained_model - 96 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 16 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 32 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 64 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 96 intel-tensorflow: mobilenetv1_int8_pretrained_model - 16 intel-tensorflow: mobilenetv1_int8_pretrained_model - 32 intel-tensorflow: mobilenetv1_int8_pretrained_model - 64 intel-tensorflow: mobilenetv1_int8_pretrained_model - 96 intel-tensorflow: inceptionv4_fp32_pretrained_model - 256 intel-tensorflow: inceptionv4_fp32_pretrained_model - 512 intel-tensorflow: inceptionv4_int8_pretrained_model - 256 intel-tensorflow: inceptionv4_int8_pretrained_model - 512 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 256 intel-tensorflow: mobilenetv1_fp32_pretrained_model - 512 intel-tensorflow: mobilenetv1_int8_pretrained_model - 256 intel-tensorflow: mobilenetv1_int8_pretrained_model - 512 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 1 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 1 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 16 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 32 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 64 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 96 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 16 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 32 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 64 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 96 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 256 zendnn-tensorflow: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - 512 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 256 zendnn-tensorflow: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - 512 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 1 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 16 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 32 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 64 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 96 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 256 zendnn-tensorflow: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - 512 a b c 6.111 75.893 210.908 206.520 14.168 119.996 716.460 698.567 64.009 2.624 4.127 3.466 2.132 1.284 0.830 0.612 0.525 0.495 0.485 0.482 139.051 7.192 547.777 1.826 264.850 252.894 247.813 245.027 800.103 824.027 830.205 803.346 247.463 249.615 779.333 774.177 61.65 17.070 104.96 10.560 1591.73 4525.90 83.63 80.42 79.15 79.05 311.78 312.25 309.86 300.91 1107.68 1047.32 952.20 914.91 4671.25 5094.81 4327.78 4293.10 80.36 81.04 288.45 290.66 867.02 856.50 3482.01 3263.94 8.070 23.159 241.34 228.35 225.81 228.11 62.78 62.82 64.63 65.86 238.11 242.65 68.24 70.36 1.586 953.78 770.81 715.78 691.01 650.17 643.44 6.165 75.807 213.399 207.373 14.091 120.306 711.29 699.652 63.903 2.613 4.091 3.449 2.126 1.278 0.825 0.610 0.522 0.494 0.485 0.481 139.519 7.168 503.500 1.986 267.531 253.600 247.959 246.187 798.920 825.337 837.083 803.687 247.089 249.918 779.241 775.258 61.00 17.296 105.50 10.351 1573.54 4539.90 83.25 80.56 79.50 79.44 319.25 315.79 309.26 303.13 1108.83 1047.98 951.88 915.83 4689.94 5078.87 4330.68 4285.76 80.40 81.07 289.43 288.25 867.13 6.111 75.612 210.656 206.943 14.234 118.000 710.846 698.009 64.060 2.627 4.117 3.468 2.134 1.285 0.831 0.612 0.525 0.496 0.486 0.483 135.894 7.360 523.171 1.924 267.660 252.684 248.704 245.605 793.970 830.170 833.729 805.521 247.448 249.763 780.454 775.557 57.42 17.200 99.00 10.445 1606.41 4528.32 82.65 80.28 79.00 78.91 312.28 311.68 314.14 297.39 1107.45 1048.20 951.85 915.29 4667.75 5092.76 4334.43 4278.58 80.20 80.95 287.48 288.82 866.92 855.61 3482.91 3272.66 8.122 23.131 240.72 226.86 226.03 230.14 62.79 62.91 64.61 65.59 239.23 243.29 68.42 70.06 1.573 953.58 768.31 717.29 690.86 650.04 642.22 OpenBenchmarking.org
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 4 - Input: Bosphorus 4K a b c 2 4 6 8 10 SE +/- 0.030, N = 3 SE +/- 0.017, N = 3 SE +/- 0.004, N = 3 6.111 6.165 6.111 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 8 - Input: Bosphorus 4K a b c 20 40 60 80 100 SE +/- 0.39, N = 3 SE +/- 0.11, N = 3 SE +/- 0.56, N = 3 75.89 75.81 75.61 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 12 - Input: Bosphorus 4K a b c 50 100 150 200 250 SE +/- 1.35, N = 3 SE +/- 0.58, N = 3 SE +/- 0.55, N = 3 210.91 213.40 210.66 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 13 - Input: Bosphorus 4K a b c 50 100 150 200 250 SE +/- 1.23, N = 3 SE +/- 0.95, N = 3 SE +/- 0.70, N = 3 206.52 207.37 206.94 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 4 - Input: Bosphorus 1080p a b c 4 8 12 16 20 SE +/- 0.02, N = 3 SE +/- 0.08, N = 3 SE +/- 0.05, N = 3 14.17 14.09 14.23 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 8 - Input: Bosphorus 1080p a b c 30 60 90 120 150 SE +/- 0.60, N = 3 SE +/- 0.75, N = 3 SE +/- 0.63, N = 3 120.00 120.31 118.00 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 12 - Input: Bosphorus 1080p a b c 150 300 450 600 750 SE +/- 5.15, N = 3 SE +/- 8.08, N = 3 SE +/- 6.31, N = 3 716.46 711.29 710.85 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.5 Encoder Mode: Preset 13 - Input: Bosphorus 1080p a b c 150 300 450 600 750 SE +/- 6.99, N = 15 SE +/- 7.24, N = 3 SE +/- 5.00, N = 15 698.57 699.65 698.01 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
Faiss Test: demo_sift1M OpenBenchmarking.org Seconds, Fewer Is Better Faiss 1.7.4 Test: demo_sift1M a b c 14 28 42 56 70 SE +/- 0.03, N = 3 SE +/- 0.02, N = 3 SE +/- 0.13, N = 3 64.01 63.90 64.06 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - PQ baseline OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - PQ baseline a b c 0.5911 1.1822 1.7733 2.3644 2.9555 SE +/- 0.005, N = 3 SE +/- 0.004, N = 3 SE +/- 0.015, N = 3 2.624 2.613 2.627 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 64 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 64 a b c 0.9286 1.8572 2.7858 3.7144 4.643 SE +/- 0.002, N = 3 SE +/- 0.008, N = 3 SE +/- 0.024, N = 3 4.127 4.091 4.117 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 62 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 62 a b c 0.7803 1.5606 2.3409 3.1212 3.9015 SE +/- 0.002, N = 3 SE +/- 0.002, N = 3 SE +/- 0.021, N = 3 3.466 3.449 3.468 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 58 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 58 a b c 0.4802 0.9604 1.4406 1.9208 2.401 SE +/- 0.001, N = 3 SE +/- 0.006, N = 3 SE +/- 0.013, N = 3 2.132 2.126 2.134 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 54 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 54 a b c 0.2891 0.5782 0.8673 1.1564 1.4455 SE +/- 0.000, N = 3 SE +/- 0.005, N = 3 SE +/- 0.008, N = 3 1.284 1.278 1.285 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 50 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 50 a b c 0.187 0.374 0.561 0.748 0.935 SE +/- 0.001, N = 3 SE +/- 0.002, N = 3 SE +/- 0.005, N = 3 0.830 0.825 0.831 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 46 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 46 a b c 0.1377 0.2754 0.4131 0.5508 0.6885 SE +/- 0.000, N = 3 SE +/- 0.003, N = 3 SE +/- 0.004, N = 3 0.612 0.610 0.612 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 42 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 42 a b c 0.1181 0.2362 0.3543 0.4724 0.5905 SE +/- 0.001, N = 3 SE +/- 0.003, N = 3 SE +/- 0.004, N = 3 0.525 0.522 0.525 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 38 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 38 a b c 0.1116 0.2232 0.3348 0.4464 0.558 SE +/- 0.000, N = 3 SE +/- 0.003, N = 3 SE +/- 0.003, N = 3 0.495 0.494 0.496 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 34 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 34 a b c 0.1094 0.2188 0.3282 0.4376 0.547 SE +/- 0.000, N = 3 SE +/- 0.003, N = 3 SE +/- 0.003, N = 3 0.485 0.485 0.486 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Faiss Test: bench_polysemous_sift1m - Polysemous 30 OpenBenchmarking.org ms per query, Fewer Is Better Faiss 1.7.4 Test: bench_polysemous_sift1m - Polysemous 30 a b c 0.1087 0.2174 0.3261 0.4348 0.5435 SE +/- 0.001, N = 3 SE +/- 0.003, N = 3 SE +/- 0.003, N = 3 0.482 0.481 0.483 1. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 1 a b c 30 60 90 120 150 SE +/- 0.67, N = 3 SE +/- 0.56, N = 3 SE +/- 1.27, N = 3 139.05 139.52 135.89
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 1 a b c 2 4 6 8 10 SE +/- 0.034, N = 3 SE +/- 0.029, N = 3 SE +/- 0.068, N = 3 7.192 7.168 7.360
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 1 a b c 120 240 360 480 600 SE +/- 5.71, N = 3 SE +/- 5.31, N = 3 SE +/- 11.17, N = 15 547.78 503.50 523.17
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 1 a b c 0.4469 0.8938 1.3407 1.7876 2.2345 SE +/- 0.019, N = 3 SE +/- 0.021, N = 3 SE +/- 0.041, N = 15 1.826 1.986 1.924
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 16 a b c 60 120 180 240 300 SE +/- 3.82, N = 3 SE +/- 1.19, N = 3 SE +/- 0.86, N = 3 264.85 267.53 267.66
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 32 a b c 60 120 180 240 300 SE +/- 0.79, N = 3 SE +/- 0.40, N = 3 SE +/- 0.53, N = 3 252.89 253.60 252.68
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 64 a b c 50 100 150 200 250 SE +/- 0.80, N = 3 SE +/- 0.54, N = 3 SE +/- 0.58, N = 3 247.81 247.96 248.70
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 96 a b c 50 100 150 200 250 SE +/- 0.71, N = 3 SE +/- 1.07, N = 3 SE +/- 0.56, N = 3 245.03 246.19 245.61
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 16 a b c 200 400 600 800 1000 SE +/- 3.01, N = 3 SE +/- 2.79, N = 3 SE +/- 4.43, N = 3 800.10 798.92 793.97
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 32 a b c 200 400 600 800 1000 SE +/- 2.87, N = 3 SE +/- 1.36, N = 3 SE +/- 2.90, N = 3 824.03 825.34 830.17
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 64 a b c 200 400 600 800 1000 SE +/- 1.51, N = 3 SE +/- 0.81, N = 3 SE +/- 2.49, N = 3 830.21 837.08 833.73
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 96 a b c 200 400 600 800 1000 SE +/- 1.57, N = 3 SE +/- 1.99, N = 3 SE +/- 0.36, N = 3 803.35 803.69 805.52
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 256 a b c 50 100 150 200 250 SE +/- 0.19, N = 3 SE +/- 0.16, N = 3 SE +/- 0.23, N = 3 247.46 247.09 247.45
Intel TensorFlow Model: resnet50_fp32_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_fp32_pretrained_model - Batch Size: 512 a b c 50 100 150 200 250 SE +/- 0.07, N = 3 SE +/- 0.12, N = 3 SE +/- 0.10, N = 3 249.62 249.92 249.76
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 256 a b c 200 400 600 800 1000 SE +/- 1.40, N = 3 SE +/- 2.42, N = 3 SE +/- 0.79, N = 3 779.33 779.24 780.45
Intel TensorFlow Model: resnet50_int8_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: resnet50_int8_pretrained_model - Batch Size: 512 a b c 200 400 600 800 1000 SE +/- 0.87, N = 3 SE +/- 0.47, N = 3 SE +/- 0.23, N = 3 774.18 775.26 775.56
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 1 a b c 14 28 42 56 70 SE +/- 0.31, N = 3 SE +/- 0.59, N = 3 SE +/- 1.97, N = 15 61.65 61.00 57.42
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 1 a b c 4 8 12 16 20 SE +/- 0.04, N = 3 SE +/- 0.25, N = 3 SE +/- 0.03, N = 15 17.07 17.30 17.20
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 1 a b c 20 40 60 80 100 SE +/- 0.54, N = 3 SE +/- 0.36, N = 3 SE +/- 3.79, N = 12 104.96 105.50 99.00
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 1 a b c 3 6 9 12 15 SE +/- 0.06, N = 3 SE +/- 0.05, N = 3 SE +/- 0.06, N = 12 10.56 10.35 10.45
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 1 a b c 300 600 900 1200 1500 SE +/- 2.41, N = 3 SE +/- 3.66, N = 3 SE +/- 0.80, N = 3 1591.73 1573.54 1606.41
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 1 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 1 a b c 1000 2000 3000 4000 5000 SE +/- 2.14, N = 3 SE +/- 2.97, N = 3 SE +/- 1.54, N = 3 4525.90 4539.90 4528.32
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 16 a b c 20 40 60 80 100 SE +/- 0.15, N = 3 SE +/- 0.74, N = 3 SE +/- 0.63, N = 3 83.63 83.25 82.65
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 32 a b c 20 40 60 80 100 SE +/- 0.10, N = 3 SE +/- 0.26, N = 3 SE +/- 0.41, N = 3 80.42 80.56 80.28
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 64 a b c 20 40 60 80 100 SE +/- 0.16, N = 3 SE +/- 0.10, N = 3 SE +/- 0.18, N = 3 79.15 79.50 79.00
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 96 a b c 20 40 60 80 100 SE +/- 0.14, N = 3 SE +/- 0.09, N = 3 SE +/- 0.16, N = 3 79.05 79.44 78.91
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 16 a b c 70 140 210 280 350 SE +/- 5.23, N = 15 SE +/- 4.04, N = 15 SE +/- 5.26, N = 12 311.78 319.25 312.28
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 32 a b c 70 140 210 280 350 SE +/- 3.42, N = 3 SE +/- 0.69, N = 3 SE +/- 3.93, N = 3 312.25 315.79 311.68
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 64 a b c 70 140 210 280 350 SE +/- 0.71, N = 3 SE +/- 2.44, N = 3 SE +/- 1.33, N = 3 309.86 309.26 314.14
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 96 a b c 70 140 210 280 350 SE +/- 2.02, N = 3 SE +/- 1.90, N = 3 SE +/- 1.86, N = 3 300.91 303.13 297.39
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 16 a b c 200 400 600 800 1000 SE +/- 0.39, N = 3 SE +/- 0.41, N = 3 SE +/- 0.62, N = 3 1107.68 1108.83 1107.45
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 32 a b c 200 400 600 800 1000 SE +/- 0.85, N = 3 SE +/- 1.16, N = 3 SE +/- 0.84, N = 3 1047.32 1047.98 1048.20
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 64 a b c 200 400 600 800 1000 SE +/- 0.93, N = 3 SE +/- 0.28, N = 3 SE +/- 0.12, N = 3 952.20 951.88 951.85
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 96 a b c 200 400 600 800 1000 SE +/- 0.59, N = 3 SE +/- 0.57, N = 3 SE +/- 0.63, N = 3 914.91 915.83 915.29
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 16 a b c 1000 2000 3000 4000 5000 SE +/- 13.68, N = 3 SE +/- 10.16, N = 3 SE +/- 30.59, N = 3 4671.25 4689.94 4667.75
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 32 a b c 1100 2200 3300 4400 5500 SE +/- 5.24, N = 3 SE +/- 21.06, N = 3 SE +/- 10.06, N = 3 5094.81 5078.87 5092.76
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 64 a b c 900 1800 2700 3600 4500 SE +/- 0.17, N = 3 SE +/- 2.46, N = 3 SE +/- 4.71, N = 3 4327.78 4330.68 4334.43
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 96 a b c 900 1800 2700 3600 4500 SE +/- 8.18, N = 3 SE +/- 1.91, N = 3 SE +/- 8.05, N = 3 4293.10 4285.76 4278.58
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 256 a b c 20 40 60 80 100 SE +/- 0.15, N = 3 SE +/- 0.09, N = 3 SE +/- 0.04, N = 3 80.36 80.40 80.20
Intel TensorFlow Model: inceptionv4_fp32_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_fp32_pretrained_model - Batch Size: 512 a b c 20 40 60 80 100 SE +/- 0.06, N = 3 SE +/- 0.11, N = 3 SE +/- 0.06, N = 3 81.04 81.07 80.95
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 256 a b c 60 120 180 240 300 SE +/- 0.52, N = 3 SE +/- 0.61, N = 3 SE +/- 0.38, N = 3 288.45 289.43 287.48
Intel TensorFlow Model: inceptionv4_int8_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: inceptionv4_int8_pretrained_model - Batch Size: 512 a b c 60 120 180 240 300 SE +/- 0.57, N = 3 SE +/- 0.10, N = 3 SE +/- 0.94, N = 3 290.66 288.25 288.82
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 256 a b c 200 400 600 800 1000 SE +/- 0.47, N = 3 SE +/- 0.10, N = 3 SE +/- 1.00, N = 3 867.02 867.13 866.92
Intel TensorFlow Model: mobilenetv1_fp32_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_fp32_pretrained_model - Batch Size: 512 a c 200 400 600 800 1000 SE +/- 0.07, N = 3 SE +/- 0.15, N = 3 856.50 855.61
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 256 a c 700 1400 2100 2800 3500 SE +/- 2.91, N = 3 SE +/- 3.88, N = 3 3482.01 3482.91
Intel TensorFlow Model: mobilenetv1_int8_pretrained_model - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better Intel TensorFlow 2.12 Model: mobilenetv1_int8_pretrained_model - Batch Size: 512 a c 700 1400 2100 2800 3500 SE +/- 1.83, N = 3 SE +/- 1.99, N = 3 3263.94 3272.66
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 1 a c 2 4 6 8 10 SE +/- 0.015, N = 3 SE +/- 0.051, N = 3 8.070 8.122
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 1 a c 6 12 18 24 30 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 23.16 23.13
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 16 a c 50 100 150 200 250 SE +/- 0.17, N = 3 SE +/- 0.45, N = 3 241.34 240.72
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 32 a c 50 100 150 200 250 SE +/- 0.13, N = 3 SE +/- 1.39, N = 3 228.35 226.86
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 64 a c 50 100 150 200 250 SE +/- 0.32, N = 3 SE +/- 1.23, N = 3 225.81 226.03
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 96 a c 50 100 150 200 250 SE +/- 1.65, N = 3 SE +/- 0.11, N = 3 228.11 230.14
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 16 a c 14 28 42 56 70 SE +/- 0.01, N = 3 SE +/- 0.03, N = 3 62.78 62.79
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 32 a c 14 28 42 56 70 SE +/- 0.11, N = 3 SE +/- 0.06, N = 3 62.82 62.91
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 64 a c 14 28 42 56 70 SE +/- 0.12, N = 3 SE +/- 0.07, N = 3 64.63 64.61
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 96 a c 15 30 45 60 75 SE +/- 0.06, N = 3 SE +/- 0.19, N = 3 65.86 65.59
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 256 a c 50 100 150 200 250 SE +/- 0.86, N = 3 SE +/- 0.13, N = 3 238.11 239.23
AMD ZenDNN TensorFlow Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_resnetv1_50_imagenet_224_224_6.97G_1.1_Z4.0 - Batch Size: 512 a c 50 100 150 200 250 SE +/- 0.55, N = 3 SE +/- 0.21, N = 3 242.65 243.29
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 256 a c 15 30 45 60 75 SE +/- 0.29, N = 3 SE +/- 0.04, N = 3 68.24 68.42
AMD ZenDNN TensorFlow Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_inceptionv4_imagenet_299_299_24.55G_1.1_Z4.0 - Batch Size: 512 a c 16 32 48 64 80 SE +/- 0.01, N = 3 SE +/- 0.08, N = 3 70.36 70.06
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 1 OpenBenchmarking.org ms, Fewer Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 1 a c 0.3569 0.7138 1.0707 1.4276 1.7845 SE +/- 0.021, N = 15 SE +/- 0.020, N = 15 1.586 1.573
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 16 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 16 a c 200 400 600 800 1000 SE +/- 1.60, N = 3 SE +/- 1.06, N = 3 953.78 953.58
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 32 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 32 a c 170 340 510 680 850 SE +/- 0.44, N = 3 SE +/- 0.39, N = 3 770.81 768.31
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 64 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 64 a c 150 300 450 600 750 SE +/- 2.05, N = 3 SE +/- 1.36, N = 3 715.78 717.29
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 96 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 96 a c 150 300 450 600 750 SE +/- 0.52, N = 3 SE +/- 0.56, N = 3 691.01 690.86
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 256 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 256 a c 140 280 420 560 700 SE +/- 0.43, N = 3 SE +/- 0.46, N = 3 650.17 650.04
AMD ZenDNN TensorFlow Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 512 OpenBenchmarking.org images/sec, More Is Better AMD ZenDNN TensorFlow 2.10 ZenDNN 4.0 Model: tf_mobilenetv1_1.0_imagenet_224_224_1.14G_1.1_Z4.0 - Batch Size: 512 a c 140 280 420 560 700 SE +/- 0.23, N = 3 SE +/- 0.86, N = 3 643.44 642.22
Phoronix Test Suite v10.8.4