Deep Learning Code Generation Fundamentals - MATLAB & Simulink (original) (raw)
Main Content
Functions, objects, and workflows that you can use to generate code for deep learning networks
You can use GPU Coder™ in tandem with the Deep Learning Toolbox™ to generate code and deploy CNN on multiple embedded platforms that use NVIDIA® or ARM® GPU processors. The Deep Learning Toolbox provides simple MATLAB® commands for creating and interconnecting the layers of a deep neural network. The availability of pretrained networks and examples such as image recognition and driver assistance applications enable you to use GPU Coder for deep learning, without expert knowledge on neural networks, deep learning, or advanced computer vision algorithms.
Apps
Objects
Code Configuration
| coder.CuDNNConfig | Parameters to configure deep learning code generation with the CUDA Deep Neural Network library |
|---|---|
| coder.TensorRTConfig | Parameters to configure deep learning code generation with the NVIDIA TensorRT library |
| coder.gpuConfig | Configuration parameters for CUDA code generation from MATLAB code by using GPU Coder |
| coder.gpuEnvConfig | Configuration object containing the parameters to check the GPU code generation environment |
Basics
Overview of CUDA® code generation workflow for convolutional neural networks.
Load Pretrained Networks for Code Generation
Create a SeriesNetwork, DAGNetwork,yolov2ObjectDetector, ssdObjectDetector, ordlnetwork object for code generation.
Supported Networks, Layers, and Classes
Networks, layers, and classes supported for code generation.
Analyze Network for Code Generation
Check code generation compatibility of a deep learning network.
Use deep learning arrays in MATLAB code intended for code generation.
dlarray Limitations for Code Generation
Adhere to code generation limitations for deep learning arrays.
Analyze Performance of Code Generated for Deep Learning Networks
Analyze and optimize the performance of the generated CUDA code for deep learning networks.
Topics
- Data Layout Considerations in Deep Learning
Fundamental data layout considerations for authoring example main functions. - Quantization of Deep Neural Networks
Understand effects of quantization and how to visualize dynamic ranges of network convolution layers. - Update Network Parameters After Code Generation
Perform post code generation updates of deep learning network parameters. - Code Generation for Deep Learning Networks by Using cuDNN
Generate code for pretrained convolutional neural networks by using the cuDNN library. - Code Generation for Deep Learning Networks by Using TensorRT
Generate code for pretrained convolutional neural networks by using the TensorRT library. - Code Generation for Deep Learning Networks Targeting ARM Mali GPUs
Generate C++ code for prediction from a deep learning network targeting an ARM Mali GPU processor. - Generated CNN Class Hierarchy
Architecture of the generated CNN class and its methods.