Best SBC for AI - Single Board Computer for Artificial Intelligence

Where once artificial intelligence (AI) was relegated to supercomputers, now makers at home may dabble with AI applications. The likes of Google AIY Vision and Voice kits enable do-it-yourselfers (DIYers) and hobbyists to create artificial intelligence applications from the comfort of their own homes. Similarly, single-board computers (SBCs) have become increasingly powerful, to the point where many development boards are capable of AI use such as machine learning and natural language processing. Check out the best single-board computers for artificial intelligence!

1. Nvidia Jetson Xavier NX - The Most Powerful Single-Board Computer for AI

Dubbed the world's smallest AI supercomputer, the Nvidia Jetson Xavier NX boasts a whopping 21 TOPS (tera operations per second) of compute on a mere 15W of power. And at just 10W, the Xavier NX clocks around 14 TOPS. That's incredibly powerful yet energy-efficient. Its ultra-small 70-45mm footprint takes up little space but packs in a 6-core NVIDIA Carmel ARM v8.2 64-bit CPU with a 384-core NVIDIA Volta GPU and 48 Tensor cores. Its spec sheet is incredibly impressive. 

In benchmarking tests, the NVIDIA Jetson Xavier NX blew its younger siblings in the Jetson Nano and Jetson TX2 out of the water. While the Jetson AGX Xavier trounced the Xavier NX, the NX is certainly no slouch. Unfortunately, it does not come cheap. Expect to shell out around $400 for the Nvidia Jetson Xavier NX, compared to $100 for the Jetson Nano. For less demanding AI applications, the Jetson Nano should work just fine. But if you need supercomputer capabilities, the NVIDIA Jetson Xavier NX is a top choice. 

Pros:

  • AI-capable - handles machine learning, natural language processing, and more
  • Great for robotics
  • Excellent for desktop use
  • Fantastic for high-end retro gaming emulation
  • Great I/O - Wi-Fi, Bluetooth, USB 3.1, GPIO, HDMI/DisplayPort
  • Up to 21 TOPS of computer power
  • 4K video output
  • Small form factor

Cons:

  • Expensive
  • Larger than Raspberry Pi and its alternatives

Nvidia Jetson Xavier NX specs:

  • CPU: 6-core NVIDIA Carmel 64-bit ARMv8.2 @ 1400MHz* (6MB L2 + 4MB L3)
  • GPU: 384-core NVIDIA Volta @ 1100MHz with 48 Tensor Cores
  • Dual NVIDIA Deep Learning Accelerator (NVDLA) engines
  • 8GB 128-bit LPDDR4x @ 1600MHz | 51.2GB/s
  • 16GB eMMC
  • 2 x DisplayPort 1.4, eDP 1.4, HDMI 2.0 @4Kp60
  • 10/100/1000 BASE-T Ethernet
  • USB 3.1, 3 x USB 2.0
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2. Raspberry Pi 4 - The Best SBC for Artificial Intelligence for Most Makers

best sbc for ai - raspberry pi 4

Clocking in at a starting price of $35, the Raspberry Pi 4 is a cost-effective maker board, and the most popular SBC on the planet. It's incredibly well-documented and supported. With a revamped system-on-a-chip (SoC) and available with 2GB, 4GB, or 8GB of DDR4 RAM, the Raspberry Pi 4 is a fantastic SBC for AI. You can image classification, object detection, and a ton of other artificial intelligence projects. TensorFlow runs much faster on the Raspberry Pi 4 than its Raspberry Pi 3 predecessor. And with USB 3.0 ports, throughput is drastically improved. 

What's more, a slew of third-party accessories transforms the Pi 4 into an excellent AI device. The Intel Neural Compute Stick 2 can be plugged into the Raspberry Pi 4 and provides an artificial intelligence framework via USB connectivity. Alternatively, the Coral Edge TPU USB accelerator pairs well with the Pi. And Google offers its AIY Vision and Voice kits that enable at-home makers to tinker with artificial intelligence, building neat projects ranging from object identification systems to autonomous vehicles. With an ultra-affordable price tag and a plethora of AI accessories such as the Intel Neural Compute Stick and Google AIY kits, the Raspberry Pi 4 is the best single-board computer for artificial intelligence for most makers. 

Pros:

  • Versatile
  • Affordable
  • Several different RAM options
  • Easy to use
  • Excellent price-to-performance ratio
  • Small footprint
  • Low power draw
  • Great OS compatibility - Linux, Android, Chrome OS, non-Linux OSes

Cons:

  • Not a true desktop replacement
  • More powerful SBCs available

Raspberry Pi 4 specs:

  • Broadcom BCM2711, Quad-core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz
  • 1GB, 2GB, 4GB, or 8GB LPDDR4-2400 SDRAM (depending on model)
  • 2.4 GHz and 5.0 GHz IEEE 802.11ac wireless, Bluetooth 5.0, BLE
  • Gigabit Ethernet
  • 2 USB 3.0 ports; 2 USB 2.0 ports.
  • Raspberry Pi standard 40 pin GPIO header (fully backwards compatible with previous boards)
  • 2 × micro-HDMI ports (up to 4kp60 supported)
  • 2-lane MIPI DSI display port
  • 2-lane MIPI CSI camera port
  • 4-pole stereo audio and composite video port
  • H.265 (4kp60 decode), H264 (1080p60 decode, 1080p30 encode)
  • OpenGL ES 3.0 graphics
  • MicroSD card slot for loading operating system and data storage
  • 5V DC via USB-C connector (minimum 3A*)
  • 5V DC via GPIO header (minimum 3A*)
  • Power over Ethernet (PoE) enabled (requires separate PoE HAT)
  • Operating temperature: 0 – 50 degrees C ambient
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3. Google Coral Dev Board - Best SBC for Machine Learning

best sbc for ai - google coral dev board

The Google Coral Dev Board is a nifty SBC for quick, easy edge computing prototyping. There's a removable system-on-module and two tera operations per second 9TOPS) per watt, both of which make the Coral Dev Board suitable for low-cost AI DIYing. At its core is an NXP i.MX 8M system-on-a-chip (SoC) with an integrated GC7000 Lite GPU. And of course, there's the Google Edge TPU co-processor, 8GB of eMMC flash storage, 1GB of LBDDR4 RAM, plus Wi-Fi and Bluetooth. The Google Coral Dev Board functions for industrial AI applications, and with a low power draw, it's easily scalable. Documentation of the Coral Dev Board as well as its Mendel operating system (OS) is top-notch. You'll find ample official and third-party resources such as help documents and sample projects. Plus, add-ons such as the Coral Camera allow for a modular experience. However, it's probably best for artificial intelligence enthusiasts with an intended purpose rather than curious newcomers. If you're merely interested in learning more about AI through hands-on experience, a Raspberry Pi 4 plus the Google Coral USB TPU accelerator is a better starting spot. 

Pros:

  • On-device machine learning with Edge TPU
  • Powerful quad-core Cortex A-53 SoC
  • 1GB DDR4

Cons:

  • Expensive
  • Not capable of desktop use

Google Coral Dev Board specs:

  • CPU: NXP i.MX 8M SOC (quad Cortex-A53, Cortex-M4F)
  • GPU: Integrated GC7000 Lite Graphics 
  • Onboard Google Edge TPU
  • 1GB LPDDR4
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4. Rock Pi N10 - A Great Single-Board Computer for Machine Learning

best sbc for ai - rock pi n10

Boasting 4GB, 6GB, or 8GB of LPDDR3, a Rockchip RK3399 with an NPU designed for AI and deep learning, the Rock Pi N10 Model A is a SBC built with artificial intelligence in mind. There's support for Linux operating systems such as Debian and even Android OSes. Its Mali T860MP4 GPU is powerful. In addition to a microSD card slot, the Rock Pi N10 sports an M.2 SSD connector that supports up to 2TB of SSD storage. Although Wi-Fi isn't baked in, the optional Rock Pi wireless module easily lets you add wireless networking to your project. There's great input/output (I/O) support with a 40-pin GPIO (general purpose input-output) header. Ultimately, the Rock Pi N10 which retails for around $100, is a great choice for hobbyists seeking to start tinkering with AI but don't want to break the bank. 

Pros:

  • Dedicated NPU with 3.0 TOPS
  • Up to 8GB of DDR3
  • Powerful RK3399Pro and Mali T860MP4
  • 16GB of eMMC
  • M.2 SSD connector
  • Full-size HDMI with 4K@60 support

Cons:

  • Only DDR3, not DDR4
  • No built-in Wi-Fi or Bluetooth

Rock Pi N10 specs:

  • CPU: RK3399Pro dual Cortex-A72, frequency 1.8GHz with quad Cortex-A53, frequency 1.4GHz
  • GPU: Mali T860MP4 GPU, OpenGL ES 1.1 /2.0 /3.0 /3.1 /3.2, Vulkan 1.0, Open CL 1.1 1.2, DX1\
  • NPU with 3.0 TOPS of compute power
  • Up to 8GB of 64-bit dual-channel LPDDR3@1866Mb/s, 3GB for CPU/GPU, 1GB for NPU
  • 16GB eMMC module, microSD card slot, M.2 SSD connector
  • Full-size HDMI 2.0 with 4K@60 support
  • MIPI DSI 2-lane connector
  • 3.5mm audio jack
  • MIPI CSI 2-lane connector
  • 1 x USB 3.0 OTG, 2 x USB 2.0
  • Gigabit LAN Ethernet port, optional Wi-Fi and Bluetooth module
  • 40-pin GPIO header
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5. HiKey 970 - Best SBC for Deep Learning

best sbc for ai - hikey970

Hailing from 96Boards, the HiKey970 concentrates on artificial intelligence. Packing a beefy HiSilicon Krin 970 SoC and an HiAI architecture, the HiKey970 includes an NPU. You'll also find LPDDR4X 1866MHz RAM, 64GB UFS 2.1 storage, Wi-Fi, Bluetooth, and GPS. And the board itself is capable of running Android and Linux. While it doesn't come cheap, selling for around $300, it's still an artificial intelligence SBC worth considering. Aside from its competency for deep learning, the HiKey970 is one of the best SBCs for robotics. 

Pros:

  • 6GB of LPDDR4 RAM
  • Quad-core Cortex-A73 and quad-core Cortex-A53 processor
  • 64GB UFS storage
  • Great connectivity - Gigabit Ethernet, GPS, PCIe gen2
  • Dedicated NPU for AI
  • Capable of AI, deep learning, robotics, and more

Cons:

  • Expensive

Hikey 970 specs:

  • Dimensions - 100 mm x 85 mm x 10 mm
  • ARM Cortex A53, ARM Cortex A73
  • Operating Supply Voltage - 12 V
  • Tool Is For Evaluation Of - Kirin 970 SoC
  • 6GB LPDDR4 RAM
  • 64GB UFS storage
  • Gigabit Ethernet
  • GPS
  • PCIe gen2
  • Dedicated NPU
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6. Best Budget AI SBC - BeagleBone AI

best sbc for ai - beaglebone ai

BeagleBoard manufactures a lineup of hardware for makers, and the aptly-named BeagleBone AI is intended for artificial intelligence. The feature-packed BeagleBone AI takes an open-source Linux-based route, serving as an intermediary between tiny, low-power single-board computers and expensive supercomputers. Based on the Texas Instruments AM5729, the BeagleBone AI includes a Dual ARM Cortex A-15 microprocessor, 2 C66x floating-point VLIW DSPs, four embedded vision engines (EVEs), a dual-core programmable real-time unit, dual-core PowerVR SGX544 3D GPU, and more. With its digital0signal-processor and embedded-vision-engine, you can unlock machine learning through the OpenCL API. Happily, the BeagleBone AI is great for industrial and at-home artificial intelligence applications. 

Pros:

  • Reasonably-priced
  • OpenCL API compatibility
  • Great connectivity - Gigabit Ethernet, dual-band Wi-Fi
  • Excellent OS compatibility - Debian-based Linux distros
  • Can handle machine learning, includes a programmable real-time unit and industrial communication system

Cons:

  • More powerful single-board computers available

BeagleBone AI specs:

  • Gigabit Ethernet, dual-band 2.4GHz/5GHz Wi-Fi
  • HDMI, USB
  • 1GB RAM
  • 16GB eMMC
  • TI C66x digital-signal-processor cores
  • Onboard embedded-vision-engine
  • OpenCL API compatibility
  • Dual-core PowerVR SGX544 3D GPU
  • Programmable real-time unit with industrial communication subsystem
  • 2.5MB on-chip L3 RAM
  • Runs Debian-based Linux distros
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Best SBCs for AI - The Best Single-Board Computers for Artificial Intelligence

Although many dev boards are inexpensive and underpowered, there are plenty of SBCs for artificial intelligence making. While some come ready to use for AI out-of-the-box such as the NVIDIA Jetson Xavier NX or Google Coral Dev Board, others such as the Raspberry Pi 4 require add-ons like a Google Coral TPU Accelerator or Intel Neural Compute Stick for AI purposes such as machine learning or deep learning. The BeagleBone AI is a cost-effective option, and the Rock Pi N10 works well too. For beginners, I'd suggest picking up a Raspberry Pi 4 and an AI add-on like the Coral Dev Accelerator or Compute Stick. If you need something dedicated, the Coral Dev Board, Rock Pi, or Jetson Nano are awesome for hobbyists. And more advanced users or commercial artificial intelligence needs might require a BeagleBone AI, HiKey970, or other more advanced SBC.

Your turn: Which SBCS for AI do you suggest?

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