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.