MATLAB Coder Interface for Deep Learning Libraries

Interface for Deep Learning Libraries from MATLAB Coder

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.

• Supported deep learning networks

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