Deploy MATLAB® algorithms as a standalone executable on the hardware
The MATLAB Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE® Platforms 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.
|Configure GPIO pin as digital input or digital output|
|Read logical value from GPIO input pin|
|Show diagram of GPIO pins|
|Write logical value to GPIO output pin|
|Connection to USB or CSI camera|
|Get a list of available cameras on the NVIDIA hardware|
|Get a list of available audio devices on the NVIDIA hardware|
|NVIDIA display object|
|Capture RGB image from Camera|
|Scan for and update the list of peripherals connected to the target hardware|
|Connection to a Velodyne LiDAR sensor|
|Acquire point clouds from |
|Start streaming point clouds from Velodyne LiDAR sensor|
|Stop streaming point clouds from Velodyne LiDAR sensor|
|Connection to USB web camera|
|Transfer file from NVIDIA hardware to host computer|
|Transfer file from host computer to target hardware|
|Delete file on 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 NVIDIA 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|
|Set the timeout value that PIL uses for reading data|
|Set the TCP/IP port number used by the PIL execution|
|Get the timeout value that PIL uses for reading data|
|Get the TCP/IP port number used by the PIL execution|
- Build and Run an Executable on NVIDIA Hardware
Build and run an executable on NVIDIA hardware.
- Build and Run an Executable on NVIDIA Hardware Using GPU Coder App
Use GPU Coder™ app to build and run an executable on NVIDIA hardware.
- Read Video Files on NVIDIA Hardware
Generate CUDA® code for reading video files on the NVIDIA target by using the
- Stop or Restart an Executable Running on NVIDIA Hardware
Stop or restart an executable running on the hardware.
- Processor-In-The-Loop Execution from Command Line
Use PIL execution to verify the numerical behavior of the generated code at the MATLAB command line.
- Processor-In-The-Loop Execution with the GPU Coder App
Use the GPU Coder app to verify the numerical behavior of the generated code.
- Execution-Time Profiling for PIL
Why measure execution times for code generated from entry-point functions.
- Targeting NVIDIA Embedded Boards (GPU Coder)
Build and deploy to NVIDIA GPU boards.
- Numerical Equivalence Testing (GPU Coder)
Compare results from model and generated code simulations.
- Parameter Tuning and Signal Monitoring by Using External Mode (GPU Coder)
Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.