Targeting NVIDIA Embedded Boards
With the MATLAB® Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE™ Platforms, you can automate the deployment of Simulink® models on embedded NVIDIA boards by building and deploying the generated code on the target hardware board. You can also remotely communicate with the target and control the peripheral devices for prototyping.
For an example of deployment to NVIDIA targets, see Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink.
Configure Model for Deployment
Configure the code generation and build process for the NVIDIA target by using the model configuration parameters:
Open the Configuration Parameters dialog box. In the left pane, select Hardware Implementation. Set Hardware board to
NVIDIA Jetson
orNVIDIA Drive
.In the Target hardware resources section, click Board Parameters, then set Device Address, Username, and Password to the values for your target hardware. The device address is the IP address or host name of the target platform.
Optionally, if you have GPU Coder™, you can configure the model to generate CUDA® code. In the left pane, click Code Generation. Set the Language parameter to
C++
and select Generate GPU Code.Click OK to save and close the Configuration Parameters dialog box.
You can also use
set_param
to configure the model parameters programmatically in the MATLAB Command Window.set_param(<modelname>,"HardwareBoard","NVIDIA Jetson"); set_param(<modelname>, "TargetLang", "C++"); set_param(<modelname>, "GenerateGPUCode", "CUDA")
Generate Code for the Model
After you set the model configuration parameters, you can generate code and deploy the executable to the target hardware.
In the Simulink Toolstrip, open the Hardware tab.
Select Build, Deploy & Start to generate and deploy the code on the hardware.
See Also
Functions
open_system
(Simulink) |load_system
(Simulink) |save_system
(Simulink) |close_system
(Simulink) |bdclose
(Simulink) |get_param
(Simulink) |set_param
(Simulink) |sim
(Simulink) |slbuild
(Simulink)
Topics
- Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink
- Accelerate Simulation Speed by Using GPU Coder
- Code Generation from Simulink Models with GPU Coder
- GPU Code Generation for Deep Learning Networks Using MATLAB Function Block
- GPU Code Generation for Blocks from the Deep Neural Networks Library
- Numerical Equivalence Testing
- Parameter Tuning and Signal Monitoring Using External Mode