GPU Coder Interface for Deep Learning Libraries
Interface for Deep Learning Libraries from GPU Coder
Updated 15 Mar 2023
GPU Coder generates optimized CUDA code from MATLAB code and Simulink models for deep learning, embedded vision, and autonomous systems. You can deploy a variety of pretrained deep learning networks such as YOLOv2, ResNet-50, SegNet, MobileNet, and others from Deep Learning Toolbox to NVIDIA GPUs. You can generate optimized code for preprocessing and postprocessing along with your trained deep learning networks to deploy complete application.
When used with GPU Coder, GPU Coder Interface for Deep Learning Libraries provides the ability for the generated code to call into cuDNN or TensorRT optimization libraries for NVIDIA GPUs.
When used in MATLAB with Deep Learning Toolbox and without GPU Coder, you can accelerate the execution of deep learning networks on NVIDIA GPUs.
This hardware support package is functional for R2018b and beyond.
If you have download or installation problems, please contact Technical Support - https://www.mathworks.com/support/contact_us.html
MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b to R2023a
Platform CompatibilityWindows macOS Linux
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