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2:19 Video length is 2:19.
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Generate CUDA Code for a Semantic Segmentation Algorithm

See an example of a DAG network used for a semantic segmentation application. Directed acyclic graph networks include popular networks, such as ResNet and GoogLeNet, for image classification or SegNet for semantic segmentation. 

Using the cnncodegen function in GPU Coder™, you can generate CUDA® code from the semantic segmentation network. You can build it into a MEX function and generate code that runs 6x faster, at about 39 frames per second, than in MATLAB®. You can also integrate the generated code into a bigger application and deploy your deep learning algorithm on embedded GPU targets such as Jetson, Tegra®, or DRIVE™ PX platforms.

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Generating CUDA Code from MATLAB (White Paper)

Bridging Wireless Communications Design and Testing with MATLAB

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Code Generation for Semantic Segmentation Network

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