Coder Radio XPS 13 ML Ubuntu Benchmark Suite
1.0.0
System
Test suite extracted from Coder Radio XPS 13 ML Ubuntu Benchmark.
pts/numenta-nab-1.1.0
-d expose
Detector: EXPoSE
pts/plaidml-1.0.4
--no-fp16 --no-train vgg16 CPU
FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
pts/plaidml-1.0.4
--no-fp16 --no-train resnet50 CPU
FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
pts/ncnn-1.1.0
-1
Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3
pts/ai-benchmark-1.0.1
Device AI Score
pts/ai-benchmark-1.0.1
Device Training Score
pts/ai-benchmark-1.0.1
Device Inference Score
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
pts/mnn-1.0.1
Model: inception-v3
pts/mnn-1.0.1
Model: mobilenet-v1-1.0
pts/mnn-1.0.1
Model: MobileNetV2_224
pts/mnn-1.0.1
Model: resnet-v2-50
pts/mnn-1.0.1
Model: SqueezeNetV1.0
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
pts/numenta-nab-1.1.0
-d earthgeckoSkyline
Detector: Earthgecko Skyline
pts/ncnn-1.1.0
-1
Target: CPU - Model: regnety_400m
pts/ncnn-1.1.0
-1
Target: CPU - Model: squeezenet_ssd
pts/ncnn-1.1.0
-1
Target: CPU - Model: yolov4-tiny
pts/ncnn-1.1.0
-1
Target: CPU - Model: resnet50
pts/ncnn-1.1.0
-1
Target: CPU - Model: alexnet
pts/ncnn-1.1.0
-1
Target: CPU - Model: resnet18
pts/ncnn-1.1.0
-1
Target: CPU - Model: vgg16
pts/ncnn-1.1.0
-1
Target: CPU - Model: googlenet
pts/ncnn-1.1.0
-1
Target: CPU - Model: blazeface
pts/ncnn-1.1.0
-1
Target: CPU - Model: efficientnet-b0
pts/ncnn-1.1.0
-1
Target: CPU - Model: mnasnet
pts/ncnn-1.1.0
-1
Target: CPU - Model: shufflenet-v2
pts/ncnn-1.1.0
-1
Target: CPU-v3-v3 - Model: mobilenet-v3
pts/ncnn-1.1.0
-1
Target: CPU-v2-v2 - Model: mobilenet-v2
pts/ncnn-1.1.0
-1
Target: CPU - Model: mobilenet
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
pts/numpy-1.2.1
pts/mlpack-1.0.2
SCIKIT_QDA
Benchmark: scikit_qda
pts/tensorflow-lite-1.0.0
--graph=squeezenet.tflite
Model: SqueezeNet
pts/tensorflow-lite-1.0.0
--graph=nasnet_mobile.tflite
Model: NASNet Mobile
pts/tensorflow-lite-1.0.0
--graph=mobilenet_v1_1.0_224_quant.tflite
Model: Mobilenet Quant
pts/tensorflow-lite-1.0.0
--graph=mobilenet_v1_1.0_224.tflite
Model: Mobilenet Float
pts/tensorflow-lite-1.0.0
--graph=inception_v4.tflite
Model: Inception V4
pts/tensorflow-lite-1.0.0
--graph=inception_resnet_v2.tflite
Model: Inception ResNet V2
pts/ncnn-1.1.0
Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3
pts/mlpack-1.0.2
SCIKIT_ICA
Benchmark: scikit_ica
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: regnety_400m
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: squeezenet_ssd
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: yolov4-tiny
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: resnet50
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: alexnet
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: resnet18
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: vgg16
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: googlenet
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: blazeface
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: efficientnet-b0
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: mnasnet
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: shufflenet-v2
pts/ncnn-1.1.0
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
pts/ncnn-1.1.0
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
pts/ncnn-1.1.0
Target: Vulkan GPU - Model: mobilenet
pts/numenta-nab-1.1.0
-d bayesChangePt
Detector: Bayesian Changepoint
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
pts/numenta-nab-1.1.0
-d relativeEntropy
Detector: Relative Entropy
pts/mlpack-1.0.2
SCIKIT_LINEARRIDGEREGRESSION
Benchmark: scikit_linearridgeregression
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
pts/mlpack-1.0.2
SCIKIT_SVM
Benchmark: scikit_svm
pts/onednn-1.6.1
--matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
pts/rnnoise-1.0.2
pts/numenta-nab-1.1.0
-d windowedGaussian
Detector: Windowed Gaussian
pts/onednn-1.6.1
--conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
pts/opencv-1.0.0
dnn
Test: DNN - Deep Neural Network
pts/scikit-learn-1.1.0
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU