Deployment

Deploy generated code to NVIDIA^® Tegra^® hardware targets

You can use GPU Coder™ with the MATLAB^® Coder™ Support Package for NVIDIA Jetson™ and NVIDIA DRIVE^® Platforms 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 support package 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.

Note

Starting in R2021a, the GPU Coder Support Package for NVIDIA GPUs is named MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms. To use this support package in R2021a, you must have the MATLAB Coder product.

Functions

`packNGo`	Package generated code in ZIP file for relocation
`codegen`	Generate C/C++ code from MATLAB code
`jetson`	Create connection to NVIDIA Jetson hardware
`drive`	Create connection to NVIDIA DRIVE hardware

Objects

`coder.hardware`	Create hardware board configuration object for C/C++ code generation from MATLAB code
`jetson`	Connection to NVIDIA Jetson hardware
`drive`	Connection 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.
GPU Profiling on NVIDIA Jetson Platforms
Analyze and optimize the performance of the generated CUDA^® code on the Jetson platform.

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 by Using External Mode
Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.
Generate CUDA ROS Node from Simulink (ROS Toolbox)
Configure Simulink^® Coder to generate and build a CUDA ROS node from a Simulink model.
Lane and Vehicle Detection in ROS Using YOLO v2 Deep Learning Algorithm (ROS Toolbox)
Use deep convolutional neural networks inside a ROS enabled Simulink model to perform lane and vehicle detection.
Sign Following Robot Using YOLOv2 Detection Algorithm with ROS in Simulink (ROS Toolbox)
Use Simulink to control a simulated robot running on a separate ROS-based simulator and generate CUDA-optimized code for the ROS node, from the Simulink model, and deploy it to the localhost device.

Build Process Management

Configure CMake Build Process
Specify CMake toolchain definition for building generated code.

Featured Examples

Getting Started with the MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms

Use the MATLAB® Coder™ Support Package for NVIDIA Jetson™ and NVIDIA DRIVE® Platforms with embedded boards from NVIDIA®. The example uses a simple vector addition algorithm to illustrate:

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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. The MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE® Platforms 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

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the MATLAB® Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE™ Platforms 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 docid:gpucoder_ug#example-gpucoderdemo_segnet.

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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

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 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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Ground Plane Segmentation and Obstacle Detection on NVIDIA Jetson Xavier NX Embedded Platforms

Ground plane segmentation of 3-D lidar data on Jetson Xavier to find obstacles near a vehicle.

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Keyword Spotting in Audio Using MFCC and LSTM Networks on NVIDIA Embedded Hardware from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson™ board for keyword spotting in audio data. This example identifies the keyword(YES) in the input audio data using Mel Frequency Cepstral Coefficients (MFCC) and a pretrained Bidirectional Long Short-Term Memory (BiLSTM) network.

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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.

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Deploy and Run Fog Rectification for Video on NVIDIA Jetson

Generate and deploy a CUDA® executable for a video-based fog rectification application. The example shows the deployable code generation capabilities that the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms provides for the MATLAB VideoReader function. This example generates a CUDA application that reads the contents of a video file, performs fog rectification operation, and displays the output video on the NVIDIA® hardware.

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Processor-in-the-Loop Execution on NVIDIA Targets Using GPU Coder

How the MATLAB® Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE™ Platforms enables the GPU Coder™ product to run PIL execution on NVIDIA DRIVE and Jetson hardware platforms. This example uses the GPU Code generation for fog rectification example from GPU Coder to demonstrate PIL execution. For more information, see docid:gpucoder_ug#example-gpucoderdemo_fog_rectification.

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Read and Write Data over Serial Port on NVIDIA Jetson Platforms

Use MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® to read and write serial data over the UART port on a Jetson board.

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Send and Receive Data over UDP on NVIDIA Jetson Platforms

Use MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® to send and receive UDP data over the network on a Jetson board.

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Send and Receive MAVLink Packets on Jetson Boards

Use MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® to send and receive MAVLink packets on a Jetson board via serial from a Pixhawk board.

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Onboard Computer Path Planning Interface for PX4 SITL Deployable on NVIDIA Jetson

Demonstrates enabling and interfacing onboard computer path planning with PX4® Software-in-the-Loop (SITL).

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Stream Camera, Depth and Semantic Segmentation Data from Unreal Engine to NVIDIA Jetson

Stream simulated camera, depth, and semantic segmentation label data from an Unreal Engine® scene to NVIDIA® Jetson™ hardware using the Video Send block in Simulink®. It then shows how to visualize incoming data streams on a monitor connected to the Jetson platform, by deploying separate models for each incoming data stream. The deployed models contain the Network Video Receive and SDL Video Display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE® Platforms.

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MODBUS TCP/IP Communication Between Client and Server Devices Using NVIDIA Jetson TX2 Hardware

Use the MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® Platforms to implement MODBUS® TCP/IP communication between MODBUS client and server devices. It also shows how to communicate between the two devices in four modes of operation, Client Read, Client Write, Server Read, and Server Write.

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Deep Learning Vehicle Detector from IP Camera Stream on Jetson

Develop a CUDA® application from a Simulink® model that performs vehicle detection using convolutional neural networks (CNN). This example takes the IP camera stream as an input, and detects vehicles in the frame. This example 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 Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox).

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Publish and Subscribe to Messages on ThingSpeak Using MQTT Blocks

Communicate using the Message Queuing Telemetry Transport (MQTT) on NVIDIA® Jetson® and DRIVE® platforms.

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Build Generated CUDA Code Using CMake Toolchain

Use CMake toolchain to build generated code.

Since R2024a
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