Deep Learning Code Generation from Simulink Applications
Generate code for pretrained deep neural networks. You can accelerate the simulation of your algorithms in Simulink® by using different execution environments. By using support packages, you can also generate and deploy C/C++ and CUDA® code on target hardware.
General Code Configuration
GPU Code Configuration
- GPU Code Generation for Deep Learning Networks Using MATLAB Function Block (GPU Coder)
Simulate and generate code for deep learning models in Simulink using MATLAB function blocks.
- GPU Code Generation for Blocks from the Deep Neural Networks Library (GPU Coder)
Simulate and generate code for deep learning models in Simulink using library blocks.
- Code Generation for a Deep Learning Simulink Model that Performs Lane and Vehicle Detection (GPU Coder)
This example shows how to develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN).
- Generate Generic C/C++ for Sequence-to-Sequence Deep Learning Simulink Models (Simulink Coder)
Generate C/C++ code for a sequence-to-sequence deep learning Simulink model.