Coder Radio XPS 13 ML Ubuntu Benchmark
Intel Core i5-1135G7 testing with a Dell 0THX8P (1.1.1 BIOS) and Intel Xe 3GB on Ubuntu 20.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2101015-FI-CODERRADI28&grs.
OpenCV
Test: DNN - Deep Neural Network
Scikit-Learn
Mlpack Benchmark
Benchmark: scikit_linearridgeregression
Mlpack Benchmark
Benchmark: scikit_svm
Mlpack Benchmark
Benchmark: scikit_qda
Mlpack Benchmark
Benchmark: scikit_ica
AI Benchmark Alpha
Device AI Score
AI Benchmark Alpha
Device Training Score
AI Benchmark Alpha
Device Inference Score
Numenta Anomaly Benchmark
Detector: Bayesian Changepoint
Numenta Anomaly Benchmark
Detector: Earthgecko Skyline
Numenta Anomaly Benchmark
Detector: Windowed Gaussian
Numenta Anomaly Benchmark
Detector: Relative Entropy
Numenta Anomaly Benchmark
Detector: EXPoSE
PlaidML
FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
PlaidML
FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
NCNN
Target: Vulkan GPU - Model: squeezenet_ssd
NCNN
Target: Vulkan GPU - Model: yolov4-tiny
NCNN
Target: Vulkan GPU - Model: resnet50
NCNN
Target: Vulkan GPU - Model: alexnet
NCNN
Target: Vulkan GPU - Model: resnet18
NCNN
Target: Vulkan GPU - Model: vgg16
NCNN
Target: Vulkan GPU - Model: googlenet
NCNN
Target: Vulkan GPU - Model: blazeface
NCNN
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: Vulkan GPU - Model: mobilenet
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU - Model: mobilenet
Mobile Neural Network
Model: inception-v3
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: resnet-v2-50
TensorFlow Lite
Model: Inception ResNet V2
TensorFlow Lite
Model: Mobilenet Quant
TensorFlow Lite
Model: Mobilenet Float
TensorFlow Lite
Model: NASNet Mobile
TensorFlow Lite
Model: Inception V4
TensorFlow Lite
Model: SqueezeNet
RNNoise
Numpy Benchmark
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
NCNN
Target: Vulkan GPU - Model: regnety_400m
NCNN
Target: Vulkan GPU - Model: efficientnet-b0
NCNN
Target: Vulkan GPU - Model: mnasnet
NCNN
Target: Vulkan GPU - Model: shufflenet-v2
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: shufflenet-v2
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: SqueezeNetV1.0
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
Phoronix Test Suite v10.8.5