GPU Coder Interface for Deep Learning

Use GPU Coder to generate optimized CUDA code for deep learning networks
Updated 19 Jun 2024
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 pre-processing and post-processing along with your trained deep learning networks to deploy complete applications.
When used with GPU Coder, GPU Coder Interface for Deep Learning 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 support package is functional for R2018b and beyond.
If you have download or installation problems, please contact Technical Support -
MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b to R2024b
Platform Compatibility
Windows macOS (Apple silicon) macOS (Intel) Linux

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