Deep Learning Code-Generierung aus Simulink-Anwendungen
Generieren Sie Code für vortrainierte tiefe neuronale Netze. Sie können die Simulation Ihrer Algorithmen in Simulink® beschleunigen, indem Sie verschiedene Ausführungsumgebungen verwenden. Durch die Verwendung von Support Packages können Sie auch C/C++ und CUDA®-Code auf der Zielhardware bereitstellen.
Modelleinstellungen
Themen
- 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.
- Generate Generic C Code Using The Stateful Predict Block in Simulink (Simulink Coder)
This example shows how to generate generic C code using the Stateful Predict block and the SIL workflow. (Seit R2024a)