ss AMD Ryzen 7 4700U testing with a LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS) and AMD Renoir 512MB on Ubuntu 23.04 via the Phoronix Test Suite. a: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 23.04, Kernel: 6.2.0-24-generic (x86_64), Desktop: GNOME Shell 44.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.0.2 (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 b: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 23.04, Kernel: 6.2.0-24-generic (x86_64), Desktop: GNOME Shell 44.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.0.2 (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 c: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 23.04, Kernel: 6.2.0-24-generic (x86_64), Desktop: GNOME Shell 44.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.0.2 (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.3.0, File-System: ext4, Screen Resolution: 1920x1080 OpenRadioss 2023.09.15 Model: Bumper Beam Seconds < Lower Is Better a . 327.20 |=================================================================== b . 329.40 |=================================================================== c . 323.25 |================================================================== OpenRadioss 2023.09.15 Model: Cell Phone Drop Test Seconds < Lower Is Better a . 193.95 |=================================================================== b . 194.05 |=================================================================== c . 193.29 |=================================================================== OpenRadioss 2023.09.15 Model: Bird Strike on Windshield Seconds < Lower Is Better a . 499.42 |=================================================================== b . 497.06 |=================================================================== c . 494.53 |================================================================== OpenRadioss 2023.09.15 Model: Rubber O-Ring Seal Installation Seconds < Lower Is Better a . 446.33 |=================================================================== b . 446.63 |=================================================================== c . 445.43 |=================================================================== OpenRadioss 2023.09.15 Model: INIVOL and Fluid Structure Interaction Drop Container Seconds < Lower Is Better a . 1253.59 |================================================================== b . 1249.12 |================================================================== c . 1253.90 |================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 Seconds < Lower Is Better a . 27.08 |==================================================================== b . 27.12 |==================================================================== c . 27.07 |==================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 Seconds < Lower Is Better a . 440.84 |=================================================================== b . 441.14 |=================================================================== c . 440.48 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 Seconds < Lower Is Better a . 1073.64 |================================================================== b . 1072.58 |================================================================== c . 1071.16 |================================================================== Embree 4.3 Binary: Pathtracer - Model: Crown Frames Per Second > Higher Is Better a . 4.7585 |=================================================================== b . 4.7405 |=================================================================== c . 4.7490 |=================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better a . 4.2922 |=================================================================== b . 4.2996 |=================================================================== c . 4.2759 |=================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Frames Per Second > Higher Is Better a . 5.6936 |=================================================================== b . 5.6994 |=================================================================== c . 5.6935 |=================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 5.1118 |=================================================================== b . 5.0954 |=================================================================== c . 5.1121 |=================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better a . 5.3249 |=================================================================== b . 5.3213 |=================================================================== c . 5.3375 |=================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 4.6132 |=================================================================== b . 4.6139 |=================================================================== c . 4.6097 |=================================================================== SVT-AV1 1.7 Encoder Mode: Preset 4 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 1.574 |==================================================================== b . 1.560 |=================================================================== c . 1.558 |=================================================================== SVT-AV1 1.7 Encoder Mode: Preset 8 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 17.27 |==================================================================== b . 17.25 |==================================================================== c . 17.03 |=================================================================== SVT-AV1 1.7 Encoder Mode: Preset 12 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 38.80 |==================================================================== b . 38.68 |==================================================================== c . 38.37 |=================================================================== SVT-AV1 1.7 Encoder Mode: Preset 13 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 42.79 |==================================================================== b . 42.29 |=================================================================== c . 42.40 |=================================================================== SVT-AV1 1.7 Encoder Mode: Preset 4 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 5.504 |==================================================================== b . 5.382 |================================================================== c . 5.327 |================================================================== SVT-AV1 1.7 Encoder Mode: Preset 8 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 47.47 |==================================================================== b . 45.57 |================================================================= c . 45.45 |================================================================= SVT-AV1 1.7 Encoder Mode: Preset 12 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 172.62 |=================================================================== b . 169.67 |================================================================== c . 168.52 |================================================================= SVT-AV1 1.7 Encoder Mode: Preset 13 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 211.72 |=================================================================== b . 211.72 |=================================================================== c . 210.31 |=================================================================== VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Fast Frames Per Second > Higher Is Better a . 2.356 |==================================================================== b . 2.348 |==================================================================== c . 2.345 |==================================================================== VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Faster Frames Per Second > Higher Is Better a . 4.947 |==================================================================== b . 4.907 |=================================================================== c . 4.926 |==================================================================== VVenC 1.9 Video Input: Bosphorus 1080p - Video Preset: Fast Frames Per Second > Higher Is Better a . 7.605 |==================================================================== b . 7.622 |==================================================================== c . 7.577 |==================================================================== VVenC 1.9 Video Input: Bosphorus 1080p - Video Preset: Faster Frames Per Second > Higher Is Better a . 17.38 |==================================================================== b . 17.34 |==================================================================== c . 17.29 |==================================================================== Intel Open Image Denoise 2.1 Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.24 |===================================================================== b . 0.24 |===================================================================== c . 0.24 |===================================================================== Intel Open Image Denoise 2.1 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.24 |===================================================================== b . 0.24 |===================================================================== c . 0.24 |===================================================================== Intel Open Image Denoise 2.1 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.12 |===================================================================== b . 0.12 |===================================================================== c . 0.12 |===================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU ISPC Items / Sec > Higher Is Better a . 89 |======================================================================= b . 89 |======================================================================= c . 89 |======================================================================= OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU Scalar Items / Sec > Higher Is Better a . 47 |======================================================================= b . 47 |======================================================================= c . 46 |===================================================================== libavif avifenc 1.0 Encoder Speed: 0 Seconds < Lower Is Better a . 270.50 |================================================================== b . 270.16 |================================================================== c . 272.77 |=================================================================== libavif avifenc 1.0 Encoder Speed: 2 Seconds < Lower Is Better a . 121.67 |================================================================== b . 123.50 |=================================================================== c . 123.82 |=================================================================== libavif avifenc 1.0 Encoder Speed: 6 Seconds < Lower Is Better a . 13.51 |=================================================================== b . 13.54 |==================================================================== c . 13.63 |==================================================================== libavif avifenc 1.0 Encoder Speed: 6, Lossless Seconds < Lower Is Better a . 19.65 |================================================================== b . 20.33 |==================================================================== c . 19.61 |================================================================== libavif avifenc 1.0 Encoder Speed: 10, Lossless Seconds < Lower Is Better a . 8.285 |==================================================================== b . 8.333 |==================================================================== c . 8.285 |==================================================================== Timed GCC Compilation 13.2 Time To Compile Seconds < Lower Is Better a . 1991.50 |================================================================== b . 1990.35 |================================================================== c . 1995.47 |================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.54 |==================================================================== b . 10.52 |==================================================================== c . 10.50 |==================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 16.36 |==================================================================== b . 16.38 |==================================================================== c . 16.35 |==================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.42484 |================================================================== b . 3.41832 |================================================================== c . 3.43182 |================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.49160 |================================================================== b . 3.49591 |================================================================== c . 3.48493 |================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 33.05 |==================================================================== b . 33.03 |==================================================================== c . 33.10 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 7.45384 |================================================================= b . 7.45289 |================================================================= c . 7.54331 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.43 |================================================================== b . 10.73 |==================================================================== c . 10.45 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 30.65 |==================================================================== b . 30.68 |==================================================================== c . 30.68 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4.36329 |================================================================== b . 4.35154 |================================================================= c . 4.39631 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 5.30540 |================================================================ b . 5.44214 |================================================================== c . 5.35481 |================================================================= oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 6461.79 |================================================================= b . 6534.40 |================================================================== c . 6523.04 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 4149.54 |================================================================== b . 4133.13 |================================================================== c . 4145.86 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 6724.77 |================================================================== b . 6686.99 |================================================================== c . 6731.81 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4113.70 |================================================================== b . 4095.25 |================================================================== c . 4079.25 |================================================================= oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 6737.34 |================================================================== b . 6696.23 |================================================================== c . 6703.84 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 4152.97 |================================================================== b . 4145.96 |================================================================== c . 4155.31 |================================================================== NCNN 20230517 Target: CPU - Model: mobilenet ms < Lower Is Better a . 23.33 |=================================================================== b . 23.67 |==================================================================== c . 23.56 |==================================================================== NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better a . 6.57 |===================================================================== b . 6.50 |==================================================================== c . 6.47 |==================================================================== NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better a . 4.53 |==================================================================== b . 4.58 |==================================================================== c . 4.63 |===================================================================== NCNN 20230517 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better a . 3.52 |==================================================================== b . 3.55 |===================================================================== c . 3.52 |==================================================================== NCNN 20230517 Target: CPU - Model: mnasnet ms < Lower Is Better a . 4.57 |===================================================================== b . 4.54 |===================================================================== c . 4.57 |===================================================================== NCNN 20230517 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better a . 8.55 |==================================================================== b . 8.65 |===================================================================== c . 8.64 |===================================================================== NCNN 20230517 Target: CPU - Model: blazeface ms < Lower Is Better a . 1.18 |==================================================================== b . 1.19 |==================================================================== c . 1.20 |===================================================================== NCNN 20230517 Target: CPU - Model: googlenet ms < Lower Is Better a . 17.65 |==================================================================== b . 17.67 |==================================================================== c . 17.78 |==================================================================== NCNN 20230517 Target: CPU - Model: vgg16 ms < Lower Is Better a . 90.47 |==================================================================== b . 90.72 |==================================================================== c . 90.54 |==================================================================== NCNN 20230517 Target: CPU - Model: resnet18 ms < Lower Is Better a . 12.64 |==================================================================== b . 12.68 |==================================================================== c . 12.65 |==================================================================== NCNN 20230517 Target: CPU - Model: alexnet ms < Lower Is Better a . 9.99 |==================================================================== b . 9.98 |==================================================================== c . 10.02 |==================================================================== NCNN 20230517 Target: CPU - Model: resnet50 ms < Lower Is Better a . 29.58 |==================================================================== b . 29.14 |=================================================================== c . 28.89 |================================================================== NCNN 20230517 Target: CPU - Model: yolov4-tiny ms < Lower Is Better a . 38.15 |==================================================================== b . 38.26 |==================================================================== c . 38.12 |==================================================================== NCNN 20230517 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better a . 18.06 |================================================================== b . 18.49 |==================================================================== c . 18.26 |=================================================================== NCNN 20230517 Target: CPU - Model: regnety_400m ms < Lower Is Better a . 8.32 |==================================================================== b . 8.48 |===================================================================== c . 8.50 |===================================================================== NCNN 20230517 Target: CPU - Model: vision_transformer ms < Lower Is Better a . 193.64 |=================================================================== b . 194.64 |=================================================================== c . 194.52 |=================================================================== NCNN 20230517 Target: CPU - Model: FastestDet ms < Lower Is Better a . 4.74 |===================================================================== b . 4.68 |==================================================================== c . 4.74 |===================================================================== OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU FPS > Higher Is Better a . 1.55 |===================================================================== b . 1.56 |===================================================================== c . 1.56 |===================================================================== OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU ms < Lower Is Better a . 2569.23 |================================================================== b . 2563.03 |================================================================== c . 2552.54 |================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU FPS > Higher Is Better a . 13.28 |=================================================================== b . 13.51 |==================================================================== c . 13.22 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU ms < Lower Is Better a . 300.95 |=================================================================== b . 295.93 |================================================================== c . 302.13 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU FPS > Higher Is Better a . 13.17 |================================================================== b . 13.55 |==================================================================== c . 13.28 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU ms < Lower Is Better a . 303.30 |=================================================================== b . 295.24 |================================================================= c . 300.85 |================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU FPS > Higher Is Better a . 94.29 |==================================================================== b . 93.82 |==================================================================== c . 93.45 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU ms < Lower Is Better a . 42.37 |=================================================================== b . 42.59 |==================================================================== c . 42.75 |==================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 2.05 |===================================================================== b . 2.06 |===================================================================== c . 2.06 |===================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 1944.19 |================================================================== b . 1940.10 |================================================================== c . 1941.23 |================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU FPS > Higher Is Better a . 382.87 |================================================================== b . 387.95 |=================================================================== c . 382.01 |================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU ms < Lower Is Better a . 10.41 |==================================================================== b . 10.28 |=================================================================== c . 10.44 |==================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU FPS > Higher Is Better a . 18.79 |==================================================================== b . 18.88 |==================================================================== c . 18.92 |==================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU ms < Lower Is Better a . 212.75 |=================================================================== b . 211.66 |=================================================================== c . 211.36 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 131.93 |=================================================================== b . 131.51 |=================================================================== c . 131.51 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 30.29 |==================================================================== b . 30.39 |==================================================================== c . 30.39 |==================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16 - Device: CPU FPS > Higher Is Better a . 149.41 |================================================================== b . 150.60 |=================================================================== c . 148.73 |================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16 - Device: CPU ms < Lower Is Better a . 26.75 |==================================================================== b . 26.53 |=================================================================== c . 26.87 |==================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16-INT8 - Device: CPU FPS > Higher Is Better a . 463.38 |=================================================================== b . 462.94 |=================================================================== c . 463.23 |=================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16-INT8 - Device: CPU ms < Lower Is Better a . 8.6 |====================================================================== b . 8.6 |====================================================================== c . 8.6 |====================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU FPS > Higher Is Better a . 61.56 |==================================================================== b . 61.50 |==================================================================== c . 61.03 |=================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU ms < Lower Is Better a . 64.94 |=================================================================== b . 64.98 |=================================================================== c . 65.48 |==================================================================== OpenVINO 2023.1 Model: Machine Translation EN To DE FP16 - Device: CPU FPS > Higher Is Better a . 17.29 |==================================================================== b . 16.95 |=================================================================== c . 17.28 |==================================================================== OpenVINO 2023.1 Model: Machine Translation EN To DE FP16 - Device: CPU ms < Lower Is Better a . 230.97 |================================================================== b . 235.81 |=================================================================== c . 231.16 |================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 204.56 |=================================================================== b . 204.54 |=================================================================== c . 203.80 |=================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 19.53 |==================================================================== b . 19.53 |==================================================================== c . 19.60 |==================================================================== OpenVINO 2023.1 Model: Person Vehicle Bike Detection FP16 - Device: CPU FPS > Higher Is Better a . 187.25 |================================================================== b . 190.52 |=================================================================== c . 190.58 |=================================================================== OpenVINO 2023.1 Model: Person Vehicle Bike Detection FP16 - Device: CPU ms < Lower Is Better a . 21.33 |==================================================================== b . 20.97 |=================================================================== c . 20.96 |=================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16 - Device: CPU FPS > Higher Is Better a . 46.52 |==================================================================== b . 46.84 |==================================================================== c . 46.07 |=================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16 - Device: CPU ms < Lower Is Better a . 85.92 |=================================================================== b . 85.32 |=================================================================== c . 86.79 |==================================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU FPS > Higher Is Better a . 2625.67 |================================================================== b . 2616.41 |================================================================== c . 2614.51 |================================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU ms < Lower Is Better a . 1.49 |===================================================================== b . 1.49 |===================================================================== c . 1.49 |===================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16-INT8 - Device: CPU FPS > Higher Is Better a . 49.86 |==================================================================== b . 48.73 |================================================================== c . 48.58 |================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16-INT8 - Device: CPU ms < Lower Is Better a . 80.18 |================================================================== b . 82.02 |==================================================================== c . 82.26 |==================================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU FPS > Higher Is Better a . 5097.25 |================================================================= b . 5149.13 |================================================================== c . 5162.26 |================================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU ms < Lower Is Better a . 0.75 |===================================================================== b . 0.75 |===================================================================== c . 0.75 |===================================================================== Apache Cassandra 4.1.3 Test: Writes Op/s > Higher Is Better BRL-CAD 7.36 VGR Performance Metric VGR Performance Metric > Higher Is Better a . 81531 |==================================================================== b . 79432 |================================================================== c . 81055 |====================================================================