Deployment

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.

Functions

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Board Connection

`jetson`	Connection to NVIDIA Jetson hardware
`drive`	Connection to NVIDIA DRIVE hardware

GPIO

`configurePin`	Configure GPIO pin as digital input or digital output
`readDigitalPin`	Read logical value from GPIO input pin
`showPins`	Show diagram of GPIO pins
`writeDigitalPin`	Write logical value to GPIO output pin

Peripherals

`camera`	Connection to USB or CSI camera
`getCameraList`	Get a list of available cameras on the NVIDIA hardware
`listAudioDevices`	Get a list of available audio devices on the NVIDIA hardware
`image`	Display image
`imageDisplay`	NVIDIA display object
`snapshot`	Capture RGB image from Camera
`updatePeripheralInfo`	Scan for and update the list of peripherals connected to the target hardware
`velodynelidar`	Connection to a Velodyne LiDAR sensor
`read`	Acquire point clouds from `velodynelidar` object buffer
`start`	Start streaming point clouds from Velodyne LiDAR sensor
`stop`	Stop streaming point clouds from Velodyne LiDAR sensor
`webcam`	Connection to USB web camera

File Operations

`getFile`	Transfer file from NVIDIA hardware to host computer
`putFile`	Transfer file from host computer to target hardware
`deleteFile`	Delete file on target hardware

Utilities

`openShell`	Open terminal on host computer to use a Linux shell on NVIDIA hardware
`system`	Run commands in a Linux shell on the NVIDIA hardware
`getL4TVersion`	Get the L4T version of the NVIDIA Jetson hardware
`getPdkorSdkVersion`	Get the version number of the DriveWorks SDK installed on the NVIDIA DRIVE hardware
`getDisplayEnvironment`	Get the display environment value used for redirecting the display on the target
`setDisplayEnvironment`	Set the display environment value used for redirecting the display on the target
`setupCodegenContext`	Select the target hardware to build code for from multiple live connection objects
`getLinuxVersion`	Get information about the Linux environment on the target
`killApplication`	Kill an application on the NVIDIA target by name
`killProcess`	Kill a process on the NVIDIA target by ID
`runApplication`	Launch an application on the NVIDIA target by name
`runExecutable`	Launch an executable on the NVIDIA target by name

Processor-in-the-Loop

`setPILTimeout`	Set the timeout value that PIL uses for reading data
`setPILPort`	Set the TCP/IP port number used by the PIL execution
`getPILTimeout`	Get the timeout value that PIL uses for reading data
`getPILPort`	Get the TCP/IP port number used by the PIL execution

Modbus Communication

`modbus`	Create a Modbus object for NVIDIA boards
`read`	Read data from a connected Modbus device
`write`	Perform a write operation to the connected Modbus device

Topics

MATLAB

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 videoReader function.
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.

Simulink

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.

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