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.
Starting in R2021a, the MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE® Platforms 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.
Configure Model for Deployment
The model configuration parameters provide many options for the code generation and build process.
Open the Configuration Parameters dialog box. Select the Hardware Implementation pane. Set the Hardware board to
NVIDIA Jetson. You can also use
Under Target hardware resources group, set the Device Address, Username, and Password of your target hardware. The device address is the IP address or host name of the target platform.
Click OK to save and close the Configuration Parameters dialog box.
You can also use
set_paramto configure the model parameter programmatically in the MATLAB Command Window.
Generate CUDA Code for the Model
Once the hardware parameters are set, in the Simulink Editor, open the Hardware tab.
Select Build, Deploy & Start to generate and deploy the code on the hardware.
- 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 by Using External Mode