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MATLAB Coder Interface for Deep Learning Libraries

Interface for Deep Learning Libraries from MATLAB Coder


Updated 18 Nov 2021

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. You can integrate the generated code into your projects as source code, static libraries, or dynamic libraries. The generated code is readable and portable.

You can deploy a variety of trained 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 algorithms.

Supported networks:

MATLAB Coder Interface for Deep Learning Libraries provides the ability for the generated code to call target-specific optimized libraries. The support package integrates with the following deep learning accelerator libraries for the corresponding CPU architectures:

• 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

This support package is functional for R2018b and beyond.

If you have download or installation problems, please contact Technical Support -

[Updates in R2019b]
1) Add VC++ 2019 compiler support for cnncodegen for MKL-DNN target
2) Add support for ONNX identity layer for all targets (ARM Neon, MKL-DNN)
3) Add support for Crop2dLayer for ARM Neon. This enables support for Fully Convolution Networks for Semantic Segmentation

[Updates in R2020b]
1) Added support for macOS

MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b to R2021b
Platform Compatibility
Windows macOS Linux

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