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从 Simulink 应用生成深度学习代码

生成用于在桌面或嵌入式目标上部署的 C/C++ 和 GPU 代码

为预训练的深度神经网络生成代码。通过使用不同的执行环境，您可以在 Simulink^® 中加速算法的仿真。通过使用支持包，您还可以生成 C/C++ 和 CUDA^® 代码并将其部署在目标硬件上。

模型设置

仿真加速

模型配置参数：仿真目标 (Simulink)

Model Configuration Parameters: GPU Acceleration (GPU Coder)

常规代码配置

Model Configuration Parameters: Code Generation Optimization (GPU Coder)

Model Configuration Parameters: Code Generation Report (GPU Coder)

Model Configuration Parameters: Comments (GPU Coder)

Model Configuration Parameters: Code Generation Custom Code (GPU Coder)

Model Configuration Parameters: Code Generation Interface (GPU Coder)

GPU 代码配置

Model Configuration Parameters: Code Generation (GPU Coder)

Model Configuration Parameters: GPU Code (GPU Coder)

Model Configuration Parameters: Code Generation Identifiers (GPU Coder)

主题

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.