GPU-Code-Generierung aus MATLAB-Anwendungen
Generieren von CUDA®-Code für die Bereitstellung auf Desktop- oder eingebetteten Zielen
Verwenden Sie GPU Coder™ zusammen mit Deep Learning Toolbox™ zur Erzeugung von CUDA-MEX- oder eigenständigem CUDA-Code, der auf Desktop- oder eingebetteten Zielen läuft. Sie können den generierten eigenständigen CUDA-Code bereitstellen, der die CUDA-Bibliothek für tiefe neuronale Netze (cuDNN), die TensorRT™-Hochleistungs-Inferenzbibliothek oder die ARM® Compute-Bibliothek für Mali GPU verwendet.
Funktionen
codegen | Generate C/C++ code from MATLAB code |
coder.getDeepLearningLayers | Get the list of layers supported for code generation for a specific deep learning library |
coder.loadDeepLearningNetwork | Load deep learning network model |
coder.loadNetworkDistributionDiscriminator | Load network distribution discriminator for code generation (Seit R2023a) |
coder.DeepLearningConfig | Create deep learning code generation configuration objects |
Apps
GPU Coder | Generate GPU code from MATLAB code |
Themen
Überblick
- Supported Networks, Layers, and Classes (GPU Coder)
Networks, layers, and classes supported for code generation. - Code Generation for dlarray (GPU Coder)
Use deep learning arrays in MATLAB® code intended for code generation. - Code Generation for Deep Learning Networks by Using cuDNN (GPU Coder)
Generate code for pretrained convolutional neural networks by using the cuDNN library. - Code Generation for Deep Learning Networks by Using TensorRT (GPU Coder)
Generate code for pretrained convolutional neural networks by using the TensorRT library. - Update Network Parameters After Code Generation (GPU Coder)
Perform post code generation updates of deep learning network parameters.
Anwendungen
- 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 Digit Images on NVIDIA GPU Using Variational Autoencoder (GPU Coder)
CUDA code generation fordlnetwork
anddlarray
objects. - Code Generation for Object Detection Using YOLO v4 Deep Learning (GPU Coder)
Generate plain CUDA code without dependencies on deep learning libraries for YOLO v4 object detector. - Code Generation for Object Detection Using YOLO v3 Deep Learning Network
This example shows how to generate CUDA® MEX for a you only look once (YOLO) v3 object detector. - Code Generation for a Deep Learning Simulink Model to Classify ECG Signals (GPU Coder)
This example demonstrates how you can use powerful signal processing techniques and Convolutional Neural Networks together to classify ECG signals. - Code Generation for Deep Learning Networks
This example shows how to generate CUDA code for an image classification application that uses deep learning. - Code Generation for a Sequence-to-Sequence LSTM Network
This example demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network. - Deep Learning Prediction on ARM Mali GPU
This example shows how to use thecnncodegen
function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs. - Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning
Generate and deploy a CUDA executable to classify electrocardiogram signals using wavelet-derived features. - Code Generation for Object Detection by Using YOLO v2
This example shows how to generate CUDA® MEX for a you only look once (YOLO) v2 object detector. - Lane Detection Optimized with GPU Coder
This example shows how to develop a deep learning lane detection application that runs on NVIDIA® GPUs. - Deep Learning Prediction with NVIDIA TensorRT Library
This example shows how to generate code for a deep learning application by using the NVIDIA® TensorRT™ library. - Traffic Sign Detection and Recognition
This example shows how to generate CUDA® MEX code for a traffic sign detection and recognition application that uses deep learning. - Logo Recognition Network
This example shows code generation for a logo classification application that uses deep learning. - Code Generation for Denoising Deep Neural Network
This example shows how to generate plain CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]). - Code Generation for Semantic Segmentation Network
This example shows code generation for an image segmentation application that uses deep learning. - Code Generation for Semantic Segmentation Network That Uses U-net
This example shows code generation for an image segmentation application that uses deep learning.