The GPU Coder™ Support Package for NVIDIA® GPUs enables you to deploy your MATLAB function on the hardware. The function is deployed as a standalone executable that continues to run even if the hardware live connection is disconnected from the host computer.
|Get a list of available cameras on the NVIDIA hardware|
|Capture RGB image from Camera|
|Create an |
|Scan for and update the list of peripherals connected to the target hardware|
|Open terminal on host computer to use a Linux shell on NVIDIA hardware|
|Run commands in a Linux shell on the NVIDIA hardware|
|Get the L4T version of the NVIDIA Jetson hardware|
|Get the version number of the DriveWorks SDK installed on the DRIVE hardware|
|Get the display environment value used for redirecting the display on the target|
|Set the display environment value used for redirecting the display on the target|
|Select the target hardware to build code for from multiple live connection objects|
|Get information about the Linux environment on the target|
|Kill an application on the NVIDIA target by name|
|Kill a process on the NVIDIA target by ID|
|Launch an application on the NVIDIA target by name|
|Launch an executable on the NVIDIA target by name|
Build and run an executable on NVIDIA hardware.
Use GPU Coder app to build and run an executable on NVIDIA hardware.
Generate CUDA code for reading video files on the NVIDIA target by using the
Stop or restart an executable running on the hardware.
Use PIL execution to verify the numerical behavior of the generated code at the MATLAB command line.
Use the GPU Coder app to verify the numerical behavior of the generated code.
Why measure execution times for code generated from entry-point functions.
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.