BeagleBoard BeagleBone® AI-64

BeagleBoard BeagleBone® AI-64

BeagleBoard BeagleBone® AI-64 presents a complete AI and Machine Learning System with the convenience and expandability of the BeagleBone platform. The BeagleBone AI-64 offers onboard peripherals to get started immediately learning and building applications.

The BeagleBoard BeagleBone AI-64 delivers a massive amount of computing power in an easy-to-use platform to build everything from autonomous robots to building automation. To start building performance-optimized embedded applications users require a locally hosted, ready-to-use, open-source-focused toolchain, and development environment, a simple web browser, power source, and network connection. Expansion possibilities are achieved through standard BeagleBone cape headers, with hundreds of open-source hardware examples and dozens of readily available embedded expansion options available off-the-shelf.


  • Expandability
    • BeagleBone® cape header compatibility for expansion with existing add-on boards
    • ikroBUS™ Shuttle header giving access to hundreds of existing Click™ sensors and actuators
  • Memory
    • 4GB LPDDR4
    • 16GB eMMC flash with high-speed interface
    • MicroSD card slot
  • High-speed interfaces
    • M.2 E-key PCIe connector to interface with Wi-Fi®/BLUETOOTH® adapters
    • USB 3.0 Type-C SuperSpeed interface for power input and data
    • 2 USB 3.0 Type-A SuperSpeed interface
    • Gigabit Ethernet
  • Camera and Display Connectors
    • Mini DisplayPort interface
    • 2x 4-Lane CSI connector for popular camera options
    • 4-Lane DSI connector for popular display options
  • User interfaces
    • 1 Boot button, 1x Reset Button, 1x Power button
    • 1 Power indication LED, 5x User LEDs
    • 5V DC input power
    • 2 UART debug
    • JTAG 10pin Tag-Connect™ for debugging
  • TDA4VM Dual 64-bit Arm® Cortex®-A72, 2.0GHz processor feature:
    • C7x floating point, vector DSP, up to 1.0GHz, 80GFLOPS, 256GOPS
    • Deep-learning matrix multiply accelerator (MMA), up to 8 TOPS (8b) at 1GHz
    • Vision Processing Accelerators (VPAC) with Image Signal Processor (ISP) and multiple vision assist accelerators
    • Depth and Motion Processing Accelerators (DMPAC)
    • Dual 64-bit Arm® Cortex-A72 microprocessor subsystem at up to 2GHz
    • 1MB shared L2 cache per dual-core Cortex-A72 cluster
    • 32KB L1 D Cache and 48KB L1 I Cache per Cortex-A72 core
    • Six Arm® Cortex-R5F MCUs at up to 1GHz
    • Two C66x floating-point DSP, up to 1.35GHz, 40GFLOPS, 160GOPS
    • 3D GPU PowerVR® Rogue™ 8XE GE8430, up to 750MHz, 96GFLOPS, 6 Gpix/sec
    • Memory subsystem with up to 8MB of on-chip L3 RAM with ECC and coherency
    • Twelve Multichannel Audio Serial Port (MCASP) modules

more information:

Please follow and like us:
Pin Share
About mixos

Mike is the founder and editor of, an electronics engineering community/news and project sharing platform. He studied Electronics and Physics and enjoys everything that has moving electrons and fun. His interests lying on solar cells, microcontrollers and switchmode power supplies. Feel free to reach him for feedback, random tips or just to say hello :-)

view all posts by admin
Notify of

Inline Feedbacks
View all comments
Get new posts by email:
Get new posts by email:

Join 97,426 other subscribers