MATLAB Coder Interface for Deep Learning

Use MATLAB Coder to generate C and C++ code for deep learning networks
Updated 19 Jun 2024
MATLAB Coder generates C and C++ code from MATLAB code for a variety of hardware platforms, from desktop systems to embedded hardware. It supports most of the MATLAB language and a wide range of toolboxes, and you can deploy a variety of pretrained deep learning networks such as YOLOv2, ResNet-50, SqueezeNet, and MobileNet from Deep Learning Toolbox. You can generate optimized code for pre-processing and post-processing along with your trained deep learning networks to deploy complete applications.
With MATLAB Coder or Simulink Coder, MATLAB Coder Interface for Deep Learning provides the ability to generate plain (library-free) C/C++ code for deep learning networks. Additionally, it provides the option to generate code that calls into the following target-specific, optimized libraries:
  • Intel oneAPI Deep Neural Network Library (oneDNN, formerly MKL-DNN): For Intel CPUs that support AVX2
  • ARM Compute Library: For ARM Cortex-A processors that support NEON instructions
When used in Simulink with Deep Learning Toolbox and without MATLAB Coder or Simulink Coder, you can accelerate simulations of Simulink models that include deep learning blocks using the Intel oneDNN optimization library.
For more information on building supported optimization libraries, please see these links:
To learn more about the recommended settings for optimizing the inference perfomance of plain, library-free C/C++ code generated from deep learning networks, please see the below link:
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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