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 preprocessing and postprocessing along with your trained deep learning networks to deploy complete applications.
When used with MATLAB Coder, MATLAB Coder Interface for Deep Learning Libraries provides the ability for the generated code to call the following target-specific optimized libraries:
- Intel Math Kernel Library for Deep Neural Networks (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, you can accelerate deep learning simulations of Simulink models using the Intel MKL-DNN optimization library.
For more information on building supported optimization libraries, please see these links:
- MATLAB Coder: How do I build the Intel MKL-DNN library for Deep Learning C++ code generation and deployment?
- MATLAB Coder: How do I build the ARM Compute Library for Deep Learning C++ code generation and deployment?
This 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
R2018b 以降 R2022b 以前と互換性あり
プラットフォームの互換性Windows macOS Linux
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