24.03.13.Pop.2204.ML.test1

AMD Ryzen 9 7950X 16-Core testing with a ASUS ProArt X670E-CREATOR WIFI (1710 BIOS) and Zotac NVIDIA GeForce RTX 4070 Ti 12GB on Pop 22.04 via the Phoronix Test Suite.

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Performance Per
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Date
Run
  Test
  Duration
Initial test 1 No water cool
March 13
  2 Days, 55 Minutes
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24.03.13.Pop.2204.ML.test1 AMD Ryzen 9 7950X 16-Core testing with a ASUS ProArt X670E-CREATOR WIFI (1710 BIOS) and Zotac NVIDIA GeForce RTX 4070 Ti 12GB on Pop 22.04 via the Phoronix Test Suite. ,,"Initial test 1 No water cool" Processor,,AMD Ryzen 9 7950X 16-Core @ 5.88GHz (16 Cores / 32 Threads) Motherboard,,ASUS ProArt X670E-CREATOR WIFI (1710 BIOS) Chipset,,AMD Device 14d8 Memory,,2 x 16 GB DDR5-4800MT/s G Skill F5-6000J3636F16G Disk,,1000GB PNY CS2130 1TB SSD Graphics,,Zotac NVIDIA GeForce RTX 4070 Ti 12GB Audio,,NVIDIA Device 22bc Monitor,,2 x DELL 2001FP Network,,Intel I225-V + Aquantia AQtion AQC113CS NBase-T/IEEE + MEDIATEK MT7922 802.11ax PCI OS,,Pop 22.04 Kernel,,6.6.10-76060610-generic (x86_64) Desktop,,GNOME Shell 42.5 Display Server,,X Server 1.21.1.4 Display Driver,,NVIDIA 550.54.14 OpenGL,,4.6.0 OpenCL,,OpenCL 3.0 CUDA 12.4.89 Vulkan,,1.3.277 Compiler,,GCC 11.4.0 File-System,,ext4 Screen Resolution,,3200x1200 ,,"Initial test 1 No water cool" "TensorFlow - Device: GPU - Batch Size: 256 - Model: VGG-16 (images/sec)",HIB,1.77 "TensorFlow - Device: GPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,5.56 "TensorFlow - Device: GPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,1.73 "TensorFlow - Device: GPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,15.90 "TensorFlow - Device: GPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,1.72 "TensorFlow - Device: GPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,15.76 "SHOC Scalable HeterOgeneous Computing - Target: OpenCL - Benchmark: Max SP Flops (GFLOPS)",HIB,43074.9 "TensorFlow - Device: GPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,35.93 "TensorFlow - Device: CPU - Batch Size: 256 - Model: VGG-16 (images/sec)",HIB,18.12 "TensorFlow - Device: GPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,5.51 "TensorFlow - Device: GPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,1.70 "TensorFlow - Device: GPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,35.82 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,36.15 "TensorFlow - Device: GPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,5.49 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,115.70 "TensorFlow - Device: GPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,15.61 "TensorFlow - Device: CPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,17.44 "TensorFlow - Device: GPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,5.42 "Numenta Anomaly Benchmark - Detector: KNN CAD (sec)",LIB,105.001 "AI Benchmark Alpha - Device AI Score (Score)",HIB,6473 "AI Benchmark Alpha - Device Training Score (Score)",HIB,3573 "AI Benchmark Alpha - Device Inference Score (Score)",HIB,2900 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,116.33 "TensorFlow - Device: GPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,15.45 "TensorFlow - Device: CPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,16.89 "TensorFlow - Device: GPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,34.84 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,36.36 "PyTorch - Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l (batches/sec)",HIB,10.44 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,10.46 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,10.59 "PyTorch - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,10.63 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,10.58 "OpenCV - Test: DNN - Deep Neural Network (ms)",LIB,30277 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,625.66 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,12.75 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,392.16 "TNN - Target: CPU - Model: DenseNet (ms)",LIB,2005.606 "Mobile Neural Network - Model: inception-v3 (ms)",LIB,23.421 "Mobile Neural Network - Model: mobilenet-v1-1.0 (ms)",LIB,2.456 "Mobile Neural Network - Model: MobileNetV2_224 (ms)",LIB,3.410 "Mobile Neural Network - Model: SqueezeNetV1.0 (ms)",LIB,4.141 "Mobile Neural Network - Model: resnet-v2-50 (ms)",LIB,12.123 "Mobile Neural Network - Model: squeezenetv1.1 (ms)",LIB,2.542 "Mobile Neural Network - Model: mobilenetV3 (ms)",LIB,1.638 "Mobile Neural Network - Model: nasnet (ms)",LIB,11.300 "TensorFlow - Device: GPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,15.10 "Numpy Benchmark - (Score)",HIB,704.52 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,16.09 "TensorFlow - Device: GPU - Batch Size: 512 - Model: VGG-16 (images/sec)",HIB, "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,17.69 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,17.66 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-152 (batches/sec)",HIB,17.59 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,17.64 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,17.66 "TensorFlow - Device: GPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,33.39 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,36.74 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,14.14 "TensorFlow - Device: GPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,1.46 "oneDNN - Harness: Recurrent Neural Network Training - Engine: CPU (ms)",LIB,1452.96 "oneDNN - Harness: Recurrent Neural Network Inference - Engine: CPU (ms)",LIB,747.499 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,388.40 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,303.3330 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,26.3345 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,54.0028 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.5143 "Mlpack Benchmark - Benchmark: scikit_qda (sec)",LIB,34.07 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,323.12 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,24.71 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,4.69 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,37.92 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,9.87 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,8.49 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,16.28 "NCNN - Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 (ms)",LIB,9.80 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,13.48 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,5.52 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,6.62 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,32.60 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,9.56 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,1.60 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,4.50 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,3.46 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,3.93 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.69 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,3.65 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,9.80 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,119.04 "NCNN - Target: Vulkan GPU - Model: FastestDet (ms)",LIB,4.30 "NCNN - Target: Vulkan GPU - Model: vision_transformer (ms)",LIB,38.12 "NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,9.83 "NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,8.38 "NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,15.84 "NCNN - Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3 (ms)",LIB,9.36 "NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,13.16 "NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,5.67 "NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,6.65 "NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,32.31 "NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,9.69 "NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,1.62 "NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,4.57 "NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,3.45 "NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,3.90 "NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.72 "NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,3.69 "NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,9.36 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,65.52 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,121.96 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,104.27 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,76.65 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,103.09 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,77.54 "TensorFlow Lite - Model: Inception V4 (us)",LIB,21139.4 "OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (ms)",LIB,11.33 "OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (FPS)",HIB,1386.93 "TensorFlow Lite - Model: Inception ResNet V2 (us)",LIB,21857.0 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,17.81 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,448.28 "TensorFlow Lite - Model: NASNet Mobile (us)",LIB,10099.3 "TensorFlow Lite - Model: Mobilenet Float (us)",LIB,1214.11 "TensorFlow Lite - Model: SqueezeNet (us)",LIB,1716.04 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,5.53 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,1442.07 "TensorFlow Lite - Model: Mobilenet Quant (us)",LIB,1861.53 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,29.32 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,272.36 "OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (ms)",LIB,4.46 "OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (FPS)",HIB,1785.48 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,21.88 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,729.99 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,5.18 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,1538.27 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,3.61 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,4335.66 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,23.94 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,667.29 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.31 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,46025.94 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,0.45 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,32402.42 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,12.91 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,618.41 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,12.61 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,1266.85 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,2.53 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,3062.63 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,6.44 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,2470.86 "TensorFlow - Device: GPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB, "TensorFlow - Device: GPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,30.67 "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,55.566 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,36.43 "oneDNN - Harness: IP Shapes 1D - Engine: CPU (ms)",LIB,1.17351 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,25.64 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-50 (batches/sec)",HIB,42.91 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,43.49 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,43.35 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,44.08 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,44.08 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,397.5797 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,20.0852 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,3.5898 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,278.3122 "spaCy - Model: en_core_web_trf (tokens/sec)",HIB,2415 "spaCy - Model: en_core_web_lg (tokens/sec)",HIB,18557 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,8.9714 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,890.1894 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,400.3931 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,19.9101 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,19.1612 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,417.1723 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.1862 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,98.0758 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,57.6038 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.3572 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,57.8304 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.2893 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,236.3144 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,33.8095 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,43.7801 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,182.6225 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,36.3089 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,27.5303 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.8023 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,92.5135 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.3590 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,96.4692 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,69.4409 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,115.1124 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,30.2415 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,264.3773 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,30.1491 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,265.2018 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,72.0412 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,110.9914 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.0556 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,90.3558 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.7601 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,173.3786 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,3.9240 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2031.8775 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.7436 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,173.8986 "DeepSpeech - Acceleration: CPU (sec)",LIB,47.03514 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,0.8214 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,1214.2399 "TensorFlow - Device: CPU - Batch Size: 512 - Model: VGG-16 (images/sec)",HIB, "oneDNN - Harness: Deconvolution Batch shapes_1d - Engine: CPU (ms)",LIB,3.06179 "PyTorch - 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