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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 (matlab coder support package for nvidia jetson and nvidia drive platforms).

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

	package generated code in zip file for relocation
`codegen`	generate c/c code from matlab code
	create connection to nvidia jetson hardware
	create connection to nvidia drive hardware

objects

	create hardware board configuration object for c/c code generation from matlab code
	connection to nvidia jetson hardware
	connection to nvidia drive hardware

topics

matlab

targeting embedded nvidia boards from the matlab command line.
targeting embedded nvidia boards by using the gpu coder app.
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.
compare results from model and generated code simulations.
tune parameters and monitor signals through a tcp/ip communication channel between development computer and target hardware.
(ros toolbox)
configure simulink^® coder to generate and build a cuda^® ros node from a simulink model.
(ros toolbox)
use deep convolutional neural networks inside a ros enabled simulink model to perform lane and vehicle detection.
(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.