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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 (MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms).


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.


packNGoPackage generated code in ZIP file for relocation
codegenGenerate C/C++ code from MATLAB code
jetsonCreate connection to NVIDIA Jetson hardware
driveCreate connection to NVIDIA DRIVE hardware


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



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.


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)

This example shows how to 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)

This example shows how to use Simulink® to control a simulated robot running on a separate ROS-based simulator.

Featured Examples