Difference between revisions of "Intel® Compute Library for Deep Neural Networks (clDNN)"
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| − | [ | + | [https://www.google.com/search?q=clDNN+openVINO Google search...] |
| − | * [ | + | * [https://github.com/intel/clDNN clDNN | GIT] |
| + | * [https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv OpenVINO Toolkit] | ||
* [[DeepLens - deep learning enabled video camera]] | * [[DeepLens - deep learning enabled video camera]] | ||
Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL Inference on Intel® Processor Graphics – including HD Graphics and Iris® Graphics. clDNN includes highly optimized building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel® processors. | Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL Inference on Intel® Processor Graphics – including HD Graphics and Iris® Graphics. clDNN includes highly optimized building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel® processors. | ||
| − | Usages supported: Image recognition, image detection, and image segmentation. | + | * Usages supported: Image recognition, image detection, and image segmentation. |
| − | Validated Topologies: AlexNet*, VGG(16,19)*, GoogleNet(v1,v2,v3)*, ResNet(50,101,152)* Faster R-CNN*, Squeezenet*, SSD_googlenet*, SSD_VGG*, PVANET*, PVANET_REID*, age_gender*, FCN* and yolo*. | + | * Validated Topologies: AlexNet*, VGG(16,19)*, GoogleNet(v1,v2,v3)*, ResNet(50,101,152)* Faster R-CNN*, Squeezenet*, SSD_googlenet*, SSD_VGG*, PVANET*, PVANET_REID*, age_gender*, FCN* and yolo*. |
| − | + | clDNN is released also together with Intel® OpenVino™ Toolkit, which contains: | |
| + | |||
| + | * Model Optimizer a Python*-based command line tool, which imports trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, and Apache MXNet*. | ||
| + | * Inference Engine an execution engine which uses a common API to deliver inference solutions on the platform of your choice (for example GPU with clDNN library) | ||
| + | |||
| + | https://software.intel.com/sites/default/files/managed/d0/fa/FPGA-Movidius-dev-workflow-700w.png | ||
Latest revision as of 20:32, 28 March 2023
Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL Inference on Intel® Processor Graphics – including HD Graphics and Iris® Graphics. clDNN includes highly optimized building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel® processors.
- Usages supported: Image recognition, image detection, and image segmentation.
- Validated Topologies: AlexNet*, VGG(16,19)*, GoogleNet(v1,v2,v3)*, ResNet(50,101,152)* Faster R-CNN*, Squeezenet*, SSD_googlenet*, SSD_VGG*, PVANET*, PVANET_REID*, age_gender*, FCN* and yolo*.
clDNN is released also together with Intel® OpenVino™ Toolkit, which contains:
- Model Optimizer a Python*-based command line tool, which imports trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, and Apache MXNet*.
- Inference Engine an execution engine which uses a common API to deliver inference solutions on the platform of your choice (for example GPU with clDNN library)
