Deployment

Deploy generated code to NVIDIA® Tegra® hardware targets

You can use GPU Coder™ with the GPU Coder Support Package for NVIDIA GPUs to deploy your MATLAB® algorithms on embedded NVIDIA GPUs. Specifically, you can target the NVIDIA Jetson and DRIVE family of boards on either Windows® or Linux® systems. The GPU Coder Support Package for NVIDIA GPUs enables you to remotely communicate with the NVIDIA target and control the peripheral devices for prototyping. The MATLAB entry-point function is deployed as a standalone executable that continues to run even if the hardware live connection is disconnected from the host computer.

To install this support package, use the Add-On Explorer in MATLAB. For information on the supported development platforms, see Install and Setup Prerequisites for NVIDIA Boards (GPU Coder Support Package for NVIDIA GPUs).

Functions

packNGoPackage generated code in ZIP file for relocation
jetsonCreate connection to NVIDIA Jetson hardware
driveCreate connection to NVIDIA DRIVE hardware

Objects

coder.hardwareCreate hardware board configuration object for C/C++ code generation from MATLAB code
jetsonConnection to NVIDIA Jetson hardware
driveConnection to NVIDIA DRIVE hardware

Topics

MATLAB

Build and Run an Executable on NVIDIA Hardware

Targeting embedded NVIDIA boards from the MATLAB command line.

Build and Run an Executable on NVIDIA Hardware Using GPU Coder App

Targeting embedded NVIDIA boards by using the GPU Coder app.

Relocate Generated Code to Another Development Environment

Package generated files into a compressed file that you can relocate and unpack with a standard zip utility.

Simulink

Targeting NVIDIA Embedded Boards

Build and deploy to NVIDIA GPU boards.

Numerical Equivalence Testing

Compare results from model and generated code simulations.

Parameter Tuning and Signal Monitoring Using External Mode

Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.

Featured Examples

Getting Started with the GPU Coder Support Package for NVIDIA GPUs

Getting Started with the GPU Coder Support Package for NVIDIA GPUs

Use the GPU Coder™ Support Package for NVIDIA GPUs and connect to NVIDIA® DRIVE™ and Jetson hardware platforms, perform basic operations, generate CUDA® executable from a MATLAB® function, and run the executable on the hardware. This example uses a simple vector addition.

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Sobel Edge Detection on NVIDIA Jetson Nano Using Raspberry Pi Camera Module V2

Sobel Edge Detection on NVIDIA Jetson Nano Using Raspberry Pi Camera Module V2

Capture and process images from a Raspberry Pi Camera Module V2 connected to the NVIDIA® Jetson Nano using the GPU Coder™ Support Package for NVIDIA GPUs. The GPU Coder Support Package for NVIDIA GPUs allows you to capture images from the Camera Module V2 and bring them into the MATLAB® environment for processing. In this example you learn how to develop a Sobel edge detection algorithm by using this capability.

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Semantic Segmentation on NVIDIA DRIVE

Semantic Segmentation on NVIDIA DRIVE

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the GPU Coder™ Support Package for NVIDIA® GPUs to deploy the executable on the NVIDIA DRIVE™ platform. This example performs code generation on the host computer and builds the generated code on the target platform by using remote build capability of the support package. For more information, see Code Generation for Semantic Segmentation Network.

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Top-Hat Filtering to Remove Uneven Background Illumination on NVIDIA Jetson TX2 Developer Kit

Top-Hat Filtering to Remove Uneven Background Illumination on NVIDIA Jetson TX2 Developer Kit

Deploy Image Processing Toolbox™ algorithms to NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. The imtophat (Image Processing Toolbox) function that performs morphological top-hat filtering on a grayscale image is used as an example to demonstrate this concept. Top-hat filtering computes the morphological opening of the image (using imopen (Image Processing Toolbox)) and then subtracts the result from the original image. The generated CUDA® code uses shared memory to speed up the operations on the GPU.

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Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

Generate CUDA® code from a DAGNetwork object and deploy the generated code onto the NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. This example uses the resnet50 deep learning network to classify images from a USB webcam video stream.

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Deploy and Classify Webcam Images on NVIDIA Jetson TX2 Platform from Simulink

Deploy and Classify Webcam Images on NVIDIA Jetson TX2 Platform from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson TX2 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 GPU Coder™ Support Package for NVIDIA GPUs to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).

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