Deep Learning Code Generation Fundamentals

R2026b

Functions, objects, and workflows that you can use to generate code for deep learning networks

Use MATLAB^® Coder™ with Deep Learning Toolbox™ to generate C or C++ code from trained deep learning network. You can generate generic C or C++ code that does not depend on third-party libraries.

Functions

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Code Generation

`codegen`	Generate C or C++ code from MATLAB code
`coder.loadDeepLearningNetwork`	Load deep learning network model
`coder.DeepLearningConfig`	Create deep learning code generation configuration objects
`coder.ai.enableParameterUpdate`	Enables run-time update of network parameters (Since R2025a)
`analyzeNetworkForCodegen`	Analyze deep learning network for code generation (Since R2022b)
`coder.regenerateDeepLearningParameters`	Regenerate files containing network learnables and states parameters

Objects

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Code Configuration Objects

`coder.DeepLearningCodeConfig`	Parameters to configure deep learning code generation that does not depend on third-party libraries
`coder.MklDNNConfig`	Parameters to configure deep learning code generation with the Intel Math Kernel Library for Deep Neural Networks

Code Configuration Parameters

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Code Generation from MATLAB

Deep learning target library	Target library for deep learning code generation
Learnables compression	Compression type
Compute precision	Compute precision

Topics

Basics

Prerequisites for Deep Learning with MATLAB Coder
Install products and configure environment for code generation for deep learning networks.
Workflow for Deep Learning Code Generation with MATLAB Coder
Generate code for prediction from a pretrained network.
Networks and Layers Supported for Code Generation
Choose a convolutional neural network that is supported for your target processor.

Code Generation

Load Pretrained Networks for Code Generation
Create a dlnetwork object, or an object detector for code generation.
Analyze Network for Code Generation
Check code generation compatibility of a deep learning network.
Generate Generic C/C++ Code for Deep Learning Networks
Generate C/C++/CUDA code for prediction from a deep learning network that does not depend on third-party libraries.
Code Generation for dlarray
Use deep learning arrays in MATLAB code intended for code generation.

Optimization and Quantization

Optimize C/C++ Code Performance for Deep Learning Applications without Deep Learning Libraries
Code generation configuration settings that optimize the performance of the generated C/C++ code for a deep learning network.
Compress Networks Learnables in bfloat16 Format
Perform learnables compression and generate C/C++ code in Brain Floating Point format, bfloat16.

Parameter Updates

Update Network Parameters at Run Time
Update deep learning network parameters at run-time without regenerating code.
Update Network Parameters After Code Generation
Perform post code generation updates of deep learning network parameters.

Code Generation for Third-Party Libraries

Code Generation for Deep Learning Networks with MKL-DNN
Generate C++ code for prediction from a deep learning network, targeting an Intel^® CPU.

Related Information

Get Started with Deep Learning Toolbox (Deep Learning Toolbox)
Deep Learning with GPU Coder (GPU Coder)

Featured Examples

Generate Digit Images Using Variational Autoencoder on Intel CPUs

Generate code for a trained VAE dlnetwork to generate hand-drawn digits.

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Cross Compile Deep Learning Code for ARM Neon Targets

Generate library or executable code on host computer for deployment on ARM^® hardware target.

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Post-Code-Generation Update of Deep Learning Network Parameters

Incrementally update the network learnables of a deep learning network application running on edge devices such as Raspberry Pi®. This example uses a cart-pole reinforcement learning application to illustrate:

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