BeagleBoard.org

BeagleBone AI-64

Expert rating7 / 10

Processor:: TI TDA4VM, Dual-Core Cortex-A72 @ 2.0GHz + C7x DSP + 6x Cortex-R5F + 2x C66x DSP
AI Accelerator:: 8 TOPS (8-bit) deep-learning matrix multiply accelerator
Memory:: 4GB LPDDR4
Storage:: 16GB eMMC, microSD
Camera/Display:: 2x 4-lane CSI camera, 4-lane DSI, Mini DisplayPort

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Manufacturer: BeagleBoard.org Last updated: 2026-10-10

BeagleBone AI-64 Review

The BeagleBone AI-64 is built around Texas Instruments' TDA4VM SoC, pairing a dual-core Cortex-A72 at 2.0GHz with a dedicated deep-learning matrix multiply accelerator (8 TOPS), a C7x DSP, six Cortex-R5F microcontrollers, and two additional C66x DSPs, with 4GB of LPDDR4 memory, 16GB of eMMC storage, Gigabit Ethernet, USB 3.0, and dual 4-lane CSI camera connectors plus a 4-lane DSI display connector and Mini DisplayPort.

It targets a genuinely different niche than every other board in our lineup, industrial robotics, computer vision, and drone applications, a heterogeneous compute platform purpose-built for real-time vision and sensor fusion workloads that a general-purpose ARM SoC handles poorly.

Heterogeneous Compute, Not Just More Cores

Where most SBCs in our lineup compete on core count and clock speed, the TDA4VM's real advantage is its mix of specialized processors, a dedicated AI accelerator, vision and depth/motion processing accelerators, and real-time microcontrollers alongside the main application cores. That's the architecture industrial robotics and drone platforms actually need: guaranteed low-latency sensor processing running alongside general compute, not just raw throughput.

Dual Camera and Display Connectivity for Vision Projects

Dual 4-lane CSI camera connectors plus a 4-lane DSI display connector and Mini DisplayPort give genuinely serious multi-camera vision project support, well beyond the single camera connector most general-purpose SBCs offer.

Comparison to Alternatives

Against our Khadas VIM4, both target camera and AI-adjacent use cases, the VIM4's Amlogic NPU and dual MIPI-CSI inputs suit hobbyist computer vision projects, while the BeagleBone AI-64's TDA4VM architecture and real-time microcontrollers are built for genuinely industrial robotics and drone deployment. Against our NVIDIA Jetson Orin Nano Super, both target AI/vision workloads, the Jetson's CUDA ecosystem and higher raw AI TOPS suit deep learning model development, the BeagleBone AI-64 suits real-time industrial sensor fusion and control specifically.

Our verdict

Reviewed by Burooj Alam. Rated by us, not by users.

Expert rating7 / 10

A purpose-built heterogeneous compute board for industrial robotics and vision work, wasted on anything general-purpose.

Best for

Industrial robotics, drone, and real-time computer vision projects needing heterogeneous compute and guaranteed low-latency sensor processing.

Not for

General-purpose computing, media centers, or hobbyist projects, this board's specialized architecture is wasted outside vision/robotics workloads.

Specifications

What these specs mean

Identity

Brand	BeagleBoard.org
Model Number	BeagleBone AI-64

Processor

SoC Model	TI TDA4VM, Dual-Core Cortex-A72 @ 2.0GHz + C7x DSP + 6x Cortex-R5F + 2x C66x DSP

Graphics and AI

NPU Present	8 TOPS (8-bit) deep-learning matrix multiply accelerator

Storage

Onboard Storage	16GB eMMC, microSD

Memory	4GB LPDDR4

Measured results

sbc-bench results

7-Zip MIPS, multi-threaded	3,470 MIPS	sbc-bench run · BeagleBone AI-64 (TI J721E); 2000 MHz; kernel 5.10; Bookworm arm64 · Source
7-Zip MIPS, single-threaded	1,872 MIPS	sbc-bench run · BeagleBone AI-64 (TI J721E); 2000 MHz; kernel 5.10; Bookworm arm64 · Source
OpenSSL AES-256-CBC, single thread	1,130,390 KB/s	sbc-bench run · BeagleBone AI-64 (TI J721E); 2000 MHz; kernel 5.10; Bookworm arm64 · Source
Memory copy bandwidth (tinymembench memcpy)	2,660 MB/s	sbc-bench run · BeagleBone AI-64 (TI J721E); 2000 MHz; kernel 5.10; Bookworm arm64 · Source

The author states these are not pure hardware numbers: they depend on kernel, distro and settings, and openssl (AES) numbers should be taken with a grain of salt. Single run, as listed in sbc-bench Results.md.

Retrieved 23 Sept 2026

sbc-bench by Thomas Kaiser (github.com/ThomasKaiser/sbc-bench, Results.md). Copyright (c) 2018, Thomas Kaiser. All rights reserved. Redistributed under the BSD 3-Clause License; full notice, conditions and disclaimer: github.com/ThomasKaiser/sbc-bench/blob/master/LICENSE

FAQs

What are the key specifications of the BeagleBone AI-64 (Single-Board Computer/Texas Instruments TDA4VM/Dual-Core Cortex-A72 2.0GHz/4GB RAM/Robotics and Vision AI Pick)?

Key specs for the BeagleBone AI-64: Storage 16GB eMMC, microSD, Processor TI TDA4VM, Dual-Core Cortex-A72 @ 2.0GHz + C7x DSP + 6x Cortex-R5F + 2x C66x DSP, AI Accelerator 8 TOPS (8-bit) deep-learning matrix multiply accelerator, Memory 4GB LPDDR4, Connectivity Gigabit Ethernet, USB 3.0 (Type-C + 2x Type-A), M.2 E-key PCIe (WiFi), Camera/Display 2x 4-lane CSI camera, 4-lane DSI, Mini DisplayPort.

Will the BeagleBone AI-64 (Single-Board Computer/Texas Instruments TDA4VM/Dual-Core Cortex-A72 2.0GHz/4GB RAM/Robotics and Vision AI Pick) work with my current setup?

The BeagleBone AI-64 (Single-Board Computer/Texas Instruments TDA4VM/Dual-Core Cortex-A72 2.0GHz/4GB RAM/Robotics and Vision AI Pick) lists Processor is TI TDA4VM, Dual-Core Cortex-A72 @ 2.0GHz + C7x DSP + 6x Cortex-R5F + 2x C66x DSP. Check that against the hardware you already own before buying, since this is the field that most often decides whether a part fits.

Who is the BeagleBone AI-64 (Single-Board Computer/Texas Instruments TDA4VM/Dual-Core Cortex-A72 2.0GHz/4GB RAM/Robotics and Vision AI Pick) best suited to?

Best suited to: Industrial robotics, drone, and real-time computer vision projects needing heterogeneous compute and guaranteed low-latency sensor processing.

What are the drawbacks of the BeagleBone AI-64 (Single-Board Computer/Texas Instruments TDA4VM/Dual-Core Cortex-A72 2.0GHz/4GB RAM/Robotics and Vision AI Pick)?

Not the best fit for: General-purpose computing, media centers, or hobbyist projects, this board's specialized architecture is wasted outside vision/robotics workloads.