This is the latest (main) BeagleBoard documentation. If you are looking for stable releases, use the drop-down menu on the bottom-left and select the desired version.

Getting Started

Hardware setup

BeagleBone® AI-64 has TI’s TDA4VM SoC which houses dual core A72, high performance vision accelerators, video codec accelerators, latest C71x and C66x DSP, high bandwidth realtime IPs for capture and display, GPU, dedicated safety island security accelerators. The SoC is power optimized to provide best in class performance for perception, sensor fusion, localization and path planning tasks in robotics, industrial and automotive applications.

For more details visit https://www.ti.com/product/TDA4VM

BeagleBone® AI-64

BeagleBone® AI-64 brings a complete system for developing artificial intelligence (AI) and machine learning solutions with the convenience and expandability of the BeagleBone® platform and the peripherals on board to get started right away learning and building applications. With locally hosted, ready-to-use, open-source focused tool chains and development environment, a simple web browser, power source and network connection are all that need to be added to start building performance-optimized embedded applications. Industry-leading expansion possibilities are enabled through familiar BeagleBone® cape headers, with hundreds of open-source hardware examples and dozens of readily available embedded expansion options available off-the-shelf.

To run the demos on BeagleBone® AI-64 you will require,

  • BeagleBone® AI-64

  • USB camera (Any V4L2 compliant 1MP/2MP camera, Eg. Logitech C270/C920/C922)

  • Full HD eDP/HDMI display

  • Minimum 16GB high performance SD card

  • 100Base-T Ethernet cable connected to internet

  • UART cable

  • External Power Supply or Power Accessory Requirements

    1. Nominal Output Voltage: 5VDC

    2. Maximum Output Current: 5000 mA

Connect the components to the SK as shown in the image.

../../../../_images/board_connections_bbai_64.jpg

Fig. 76 BeagleBone® AI-64 for Edge AI connections

USB Camera

UVC (USB video class) compliant USB cameras are supported on the BeagleBone® AI-64. The driver for the same is enabled in linux image. The linux image has been tested with C270/C920/C922 versions of Logitech USB cameras. Please refer to pub_edgeai_multiple_usb_cams to stream from multiple USB cameras simultaneously.

IMX219 Raw sensor

IMX219 camera module from Raspberry pi / Arducam is supported by BeagleBone® AI-64. It is a 8MP sensor with no ISP, which can transmit raw SRGGB8 frames over CSI lanes at 1080p 60 fps. This camera module can be ordered from https://www.amazon.com/Raspberry-Pi-Camera-Module-Megapixel/dp/B01ER2SKFS The camera can be connected to any of the 2 RPi zero 22 pin camera headers on BB AI-64 as shown below

Note that the headers have to be lifted up to connect the cameras

Note

To be updated By default IMX219 is disabled. After connecting the camera you can enable it by specifying the dtb overlay file in /run/media/mmcblk0p1/uenv.txt as below,

name_overlays=k3-j721e-edgeai-apps.dtbo k3-j721e-sk-rpi-cam-imx219.dtbo

Reboot the board after editing and saving the file.

Two RPi cameras can be connected to 2 headers for multi camera use-cases

Please refer pub_edgeai_camera_sources to know how to list all the cameras connected and select which one to use for the demo.

By default imx219 will be configured to capture at 8 bit, but it also supports 10 bit capture in 16 bit container. To use it in 10 bit mode, below steps are required:

  • Modify the /opt/edge_ai_apps/scripts/setup_cameras.sh to set the format to 10 bit like below

CSI_CAM_0_FMT='[fmt:SRGGB8_1X10/1920x1080]'
CSI_CAM_1_FMT='[fmt:SRGGB8_1X10/1920x1080]'

  • Change the imaging binaries to use 10 bit versions

mv /opt/imaging/imx219/dcc_2a.bin /opt/imaging/imx219/dcc_2a_8b.bin
mv /opt/imaging/imx219/dcc_viss.bin /opt/imaging/imx219/dcc_viss_8b.bin
mv /opt/imaging/imx219/dcc_2a_10b.bin /opt/imaging/imx219/dcc_2a.bin
mv /opt/imaging/imx219/dcc_viss_10b.bin /opt/imaging/imx219/dcc_viss.bin

  • Set the input format in the /opt/edge_ai_apps/configs/rpiV2_cam_example.yaml as rggb10

Software setup

Preparing SD card image

Download the bullseye-xfce-edgeai-arm64 image from the links below and flash it to SD card using Balena etcher tool.

The Balena etcher tool can be installed either on Windows/Linux. Just download the etcher image and follow the instructions to prepare the SD card.

../../../../_images/balena_etcher.png

Fig. 77 Balena Etcher tool to flash SD card with Processor linux image Linux for Edge AI

The etcher image is created for 16 GB SD cards, if you are using larger SD card, it is possible to expand the root filesystem to use the full SD card capacity using below steps

#find the SD card device entry using lsblk (Eg: /dev/sdc)
#use the following commands to expand the filesystem
#Make sure you have write permission to SD card or run the commands as root

#Unmount the BOOT and rootfs partition before using parted tool
umount /dev/sdX1
umount /dev/sdX2

#Use parted tool to resize the rootfs partition to use
#the entire remaining space on the SD card
#You might require sudo permissions to execute these steps
parted -s /dev/sdX resizepart 2 '100%'
e2fsck -f /dev/sdX2
resize2fs /dev/sdX2

#replace /dev/sdX in above commands with SD card device entry

Power ON and Boot

Ensure that the power supply is disconnected before inserting the SD card. Once the SD card is firmly inserted in its slot and the board is powered ON, the board will take less than 20sec to boot and display a wallpaper as shown in the image below.

You can also view the boot log by connecting the UART cable to your PC and use a serial port communications program.

For Linux OS minicom works well. Please refer to the below documentation on ‘minicom’ for more details.

https://help.ubuntu.com/community/Minicom

When starting minicom, turn on the colors options like below:

sudo minicom -D /dev/ttyUSB2 -c on

For Windows OS Tera Term works well. Please refer to the below documentation on ‘TeraTerm’ for more details

https://learn.sparkfun.com/tutorials/terminal-basics/tera-term-windows

Note

Baud rate should be configured to 115200 bps in serial port communication program. You may not see any log in the UART console if you connect to it after the booting is complete or login prompt may get lost in between boot logs, press ENTER to get login prompt

As part of the linux systemd /opt/edge_ai_apps/init_script.sh is executed which does the below,

  • This kills weston compositor which holds the display pipe. This step will make the wallpaper showing on the display disappear and come back

  • The display pipe can now be used by ‘kmssink’ GStreamer element while running the demo applications.

  • The script can also be used to setup proxies if connected behind a firewall.

Once Linux boots login as root user with no password.

Connect remotely

If you don’t prefer the UART console, you can also access the device with the IP address that is shown on the display.

With the IP address one can ssh directly to the board, view the contents and run the demos.

For best experience we recommend using VSCode which can be downloaded from here.

https://code.visualstudio.com/download

You also require the “Remote development extension pack” installed in VSCode as mentioned here:

https://code.visualstudio.com/docs/remote/ssh