Simulink를 사용한 딥러닝 코드 생성
GPU Coder를 사용하면 여러 가지의 훈련된 딥러닝 신경망 포함된 Simulink 모델에 대한 최적화된 코드를 생성할 수 있습니다. MATLAB을 사용하여 Simulink에서 딥러닝 기능을 구현할 수 있습니다.
모델 설정
도움말 항목
- 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.
추천 예제
Generate Code for 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 on printed circuit boards (PCBs).
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 YOLO v2 딥러닝을 사용한 객체 검출 (Computer Vision Toolbox).
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 웨이블릿 분석 및 딥러닝을 사용하여 시계열 분류하기 (Wavelet Toolbox).
