Deep Learning Code Generation with Simulink

Deep learning code generation workflows using Simulink

With GPU Coder, you can generate optimized code for Simulink models that contains a variety of trained deep learning networks. You can implement the deep learning functionality in Simulink by using MATLAB.

Model Settings

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Topics

GPU Code Generation for Deep Learning Networks Using MATLAB Function Block
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
Simulate and generate code for deep learning models in Simulink using library blocks.
Targeting NVIDIA Embedded Boards
Build and deploy to NVIDIA GPU boards.

Featured Examples

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Code Generation for a Deep Learning Simulink Model that Detect Defects on Printed Circuit Boards Using YOLOX Network

Generate CUDA code for a simulink model that detects. localizes, and classifies defects in printed circuit boards (PCBs).

Since R2024b
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Code Generation for a Deep Learning Simulink Model That Performs Lane and Vehicle Detection

Develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN). This example takes the frames of a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame. This example uses the pretrained lane detection network from the Lane Detection Optimized with GPU Coder example of the GPU Coder™ product. For more information, see Lane Detection Optimized with GPU Coder. This example also uses the pretrained vehicle detection network from the Object Detection Using YOLO v2 Deep Learning example of the Computer Vision Toolbox™. For more information, see Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox).

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Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson™ board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network, ResNet-50. The Simulink model in the example uses the camera and display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE™ Platforms to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

Code Generation for a Deep Learning Simulink Model to Classify ECG Signals

Demonstrates how you can use powerful signal processing techniques and Convolutional Neural Networks together to classify ECG signals. We will also showcase how CUDA® code can be generated from the Simulink® model. This example uses the pretrained CNN network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

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