Intel® Compute Library for Deep Neural Networks (clDNN) - Revision history

BPeat: Text replacement - "http:" to "https:"

2023-03-29T00:32:43Z

Text replacement - "http:" to "https:"

BPeat at 17:19, 28 May 2018

2018-05-28T17:19:35Z

BPeat at 17:11, 28 May 2018

2018-05-28T17:11:43Z

BPeat at 17:08, 28 May 2018

2018-05-28T17:08:41Z

BPeat: BPeat moved page Intel® clDNN to Intel® Compute Library for Deep Neural Networks (clDNN) without leaving a redirect

2018-05-28T17:05:25Z

BPeat moved page Intel® clDNN to Intel® Compute Library for Deep Neural Networks (clDNN) without leaving a redirect

BPeat: Created page with "[http://www.google.com/search?q=clDNN+intel YouTube search...] * [http://github.com/intel/clDNN clDNN | GIT] * DeepLens - deep learning enabled video camera Compute Libr..."

2018-05-28T17:04:50Z

Created page with "[http://www.google.com/search?q=clDNN+intel YouTube search...] * [http://github.com/intel/clDNN clDNN | GIT] * DeepLens - deep learning enabled video camera Compute Libr..."

New page

[http://www.google.com/search?q=clDNN+intel YouTube search...]

* [http://github.com/intel/clDNN clDNN | GIT]
* [[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.

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*.

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-[http://www.google.com/search?q=clDNN+openVINO Google search...]
+[https://www.google.com/search?q=clDNN+openVINO Google search...]
-* [http://github.com/intel/clDNN clDNN | GIT]
+* [https://github.com/intel/clDNN clDNN | GIT]
-* [http://software.intel.com/en-us/openvino-toolkit/deep-learning-cv OpenVINO Toolkit]
+* [https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv OpenVINO Toolkit]
 * [[DeepLens - deep learning enabled video camera]]

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-[http://www.google.com/search?q=clDNN+intel YouTube search...]
+[http://www.google.com/search?q=clDNN+openVINO Google search...]
 * [http://github.com/intel/clDNN clDNN | GIT]
-* [http://software.intel.com/en-us/openvino-toolkit/deep-learning-cv OPENVINO Toolkit]
+* [http://software.intel.com/en-us/openvino-toolkit/deep-learning-cv OpenVINO Toolkit]
 * [[DeepLens - deep learning enabled video camera]]
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 https://software.intel.com/sites/default/files/managed/d0/fa/FPGA-Movidius-dev-workflow-700w.png

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 * [http://github.com/intel/clDNN clDNN | GIT]
 * [[DeepLens - deep learning enabled video camera]]
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 * 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)
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 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*.
 <youtube>xwESWqdJt_Y</youtube>

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