
BeagleBone AI-64
Open-source SBC powered by the TI TDA4VM automotive AI SoC with 8 TOPS neural accelerator, 4GB LPDDR4, and Gigabit Ethernet, designed for robotics and edge AI development.
$185.63
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Specifications
Components
TI TDA4VM automotive-grade SoC with dual Cortex-A72 @ 2GHz + dual Cortex-R5F @ 1GHz and 8 TOPS MMA neural accelerator.
Overview
The BeagleBone AI-64 is an open-source single-board computer from the BeagleBoard.org Foundation, built around the Texas Instruments TDA4VM System-on-Chip — an automotive-grade processor originally designed for ADAS (Advanced Driver Assistance Systems). At its core are dual Arm Cortex-A72 application processors running alongside dual Cortex-R5F real-time cores, providing a powerful combination of general-purpose Linux computing and deterministic real-time control.
The standout feature of the AI-64 is its 8 TOPS MMA (Matrix Multiply Accelerator) neural processing engine, which enables hardware-accelerated inferencing for computer vision, object detection, and other machine learning workloads entirely at the edge. The board ships with 4GB of LPDDR4 RAM and features Gigabit Ethernet, a USB-C SuperSpeed port, and the traditional BeagleBone 92-pin expansion headers that make it compatible with a wide ecosystem of capes and add-on boards.
Running Armbian or the official Debian-based BeagleBoard image, the AI-64 is suited for robotics prototyping, autonomous system development, and AI/ML research that requires a balance of Linux flexibility and real-time responsiveness. Its open-source hardware design, combined with TI's Processor SDK ecosystem, makes it a compelling platform for teams transitioning edge AI applications from lab to production.
Use Cases
4Edge AI
On-device machine learning inference at the edge, without cloud dependency
Robotics
General robotics applications including mobile robots, robotic arms, drones, and automated systems.
Computer Vision
Image recognition, object detection, and ML inference using embedded cameras and AI-capable processors.
adas-prototyping
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