mirror of https://github.com/LCTT/TranslateProject.git synced 2024-12-26 21:30:55 +08:00

DarkSun 9c5f690e0b 选题[tech]: 20210201 Best Single Board Computers for AI and Deep Learning Projects

sources/tech/20210201 Best Single Board Computers for AI and Deep Learning Projects.md

2021-02-01 05:03:41 +08:00

14 KiB

Raw Blame History

Best Single Board Computers for AI and Deep Learning Projects

Single-board computers (SBC) are very popular with tinkerers and hobbyists alike, they offer a lot of functionality in a very small form factor. An SBC has the CPU, GPU, memory, IO ports, etc. on a small circuit board and users can add functionality by adding new devices to the GPIO ports. Some of the more popular SBCs include the Raspberry Pi and Arduino family of products.

However, there is an increasing demand for SBC’s that can be used for edge compute applications like Artificial Intelligence (AI) or Deep Learning (DL) and there are quite a few. The list below consists of some of the best SBCs that have been developed for edge computing.

The list is in no particular order of ranking. Some links here are affiliate links. Please read our affiliate policy.

1. Nvidia Jetson Family

Nvidia has a great lineup of SBCs that cater to AI developers and hobbyists alike. Their line of “Jetson Developer Kits” are some of the most powerful and value for money SBCs available in the market. Below is a list of their offerings.

Nvidia Jetson Nano Developer Kit

Starting at $59, the Jetson Nano is the cheapest SBC in the list and offers a good price to performance ratio. It can run multiple neural networks alongside other applications such as object detection, segmentation, speech processing and image classification.

The Jetson Nano is aimed towards AI enthusiasts, hobbyists and developers who want to do projects by implementing AI.

The Jetson Nano is being offered in two variants: 4 GB and 2 GB. The main differences between the two are, the price, RAM capacity and IO ports being offered. The 4 GB variant has been showcased in the image above.

Key Specifications

CPU: Quad-core ARM A57 @ 1.43 GHz
GPU: 128-core NVIDIA Maxwell
Memory: 4 GB 64-bit LPDDR4 @ 25.6 GB/s or 2 GB 64-bit LPDDR4 @ 25.6 GB/s
Storage: microSD card support
Display: HDMI and Display Port or HDMI

Preview	Product	Price
$NVIDIA Jetson Nano 2GB Developer Kit $945-13541-0000-000$$ $NVIDIA Jetson Nano 2GB Developer Kit $945-13541-0000-000$$	[NVIDIA Jetson Nano 2GB Developer Kit (945-13541-0000-000)][9]	$59.00[][10]	[Buy on Amazon][11]

Nvidia Jetson Xavier NX Developer Kit

![][12]

The Jetson Xavier NX is a step up from the Jetson Nano and is aimed more towards OEMs, start-ups and AI developers.

The Jetson Xavier NX is meant for applications that need more serious AI processing power that an entry level offering like the Jetson Nano simply can’t deliver. The Jetson Xavier NX is being offered at $386.99.

Key Specifications

CPU: 6-core NVIDIA Carmel ARM v8.2 64-bit CPU
GPU: NVIDIA Volta architecture with 384 NVIDIA CUDA cores and 48 Tensor cores
DL Accelerator: 2x NVDLA Engines
Vision Accelerator: 7-Way VLIW Vision Processor
Memory: 8 GB 128-bit LPDDR4x @ 51.2 GB/s
Storage: microSD support
Display: HDMI and Display Port

Preview	Product	Price
![NVIDIA Jetson Xavier NX Developer Kit (812674024318)][13] ![NVIDIA Jetson Xavier NX Developer Kit (812674024318)][13]	[NVIDIA Jetson Xavier NX Developer Kit (812674024318)][14]	$386.89[][10]	[Buy on Amazon][15]

Nvidia Jetson AGX Xavier Developer Kit

![][16]

The Jetson AGX Xavier is the flagship product of the Jetson family, it is meant to be deployed in servers and AI robotics applications in industries such as manufacturing, retail, automobile, agriculture, etc.

Coming in at $694.91, the Jetson AGX Xavier is not meant for beginners, it is meant for developers who want top-tier edge compute performance at their disposal and for companies who want good scalability for their applications.

Key Specifications

CPU: 8-core ARM v8.2 64-bit CPU
GPU: 512-core Volta GPU with Tensor Cores
DL Accelerator: 2x NVDLA Engines
Vision Accelerator: 7-Way VLIW Vision Processor
Memory: 32 GB 256-Bit LPDDR4x @ 137 GB/s
Storage: 32 GB eMMC 5.1 and uSD/UFS Card Socket for storage expansion
Display: HDMI 2.0

Preview	Product	Price
![NVIDIA Jetson AGX Xavier Developer Kit (32GB)][17] ![NVIDIA Jetson AGX Xavier Developer Kit (32GB)][17]	[NVIDIA Jetson AGX Xavier Developer Kit (32GB)][18]	$694.91[][10]	[Buy on Amazon][19]

2. ROCK Pi N10

![][20]

The ROCK Pi N10, developed by [Radxa][21] is the second cheapest offering in this list with its base variant coming in at $99, its range topping variant comes in at $169,

The ROCK Pi N10 is equipped with a NPU (Neural Processing Unit) that helps it in processing AI/ Deep Learning workloads with ease. It offers up to 3 TOPS (Tera Operations Per Second) of performance.

It is being offered in three variants namely, ROCK Pi N10 Model A, ROCK Pi N10 Model B, ROCK Pi N10 Model C, the only differences between these variants are the price, RAM and Storage capacities.

The ROCK Pi N10 is available for purchase through [Seeed Studio][22].

Key Specifications

CPU: RK3399Pro with 2-core Cortex-A72 @ 1.8 GHz and 4-Core Cortex-A53 @ 1.4 GHz
GPU: Mali T860MP4
NPU: Supports 8bit/16bit computing with up to 3.0 TOPS computing power
Memory: 4 GB/6 GB/8 GB 64-bit LPDDR3 @ 1866 Mb/s
Storage: 16 GB/32 GB/64 GB eMMC
Display: HDMI 2.0

3. BeagleBone AI

![][23]

The BeagleBone AI is [BeagleBoard.org][24]‘s open source SBC is meant to bridge the gap between small SBCs and more powerful industrial computers. The hardware and software of the BeagleBoard are completely open source.

It is meant for use in the automation of homes, industries and other commercial use cases. It is priced at ~$110, the price varies across dealers, for more info check [their website][25].

Key Specifications

CPU: Texas Instrument AM5729 with Dual-core ARM Cortex-A15 @ 1.5GHz
Co-Processor: 2 x Dual-core ARM Cortex-M4
DSP: 2 x C66x floating-point VLIW
EVE: 4 x Embedded Vision Engines
GPU: PowerVR SGX544
RAM: 1 GB
Storage: 16 GB eMMC
Display: microHDMI

Preview	Product	Price
![BeagleBone AI][26] ![BeagleBone AI][26]	[BeagleBone AI][27]	$127.49[][10]	[Buy on Amazon][28]

4. BeagleV

![][29]

The BeagleV is the latest launch in the list, it is an SBC that runs Linux out of the box and has a [RISC-V][30] CPU.

It is capable of running edge compute applications effortlessly, to know more about the BeagleV check [our coverage][31] of the launch.

The BeagleV will be getting two variants, a 4 GB RAM variant and an 8 GB RAM variant. Pricing starts at $119 for the base model and $149 for the 8 GB RAM model, it is up for pre-order through [their website][32].

Key Specifications

CPU: RISC-V U74 2-Core @ 1.0GHz
DSP: Vision DSP Tensilica-VP6
DL Accelerator: NVDLA Engine 1-core
NPU: Neural Network Engine
RAM: 4 GB/8 GB (2 x 4 GB) LPDDR4 SDRAM
Storage: microSD slot
Display: HDMI 1.4

5. HiKey970

![][33]

HiKey970 is [96 Boards][34] first SBC meant for edge compute applications and is the world’s first dedicated NPU AI platform.

The HiKey970 features an CPU, GPU and an NPU for accelerating AI performance, it can also be used for training and building DL (Deep Learning) models.

The HiKey970 is priced at $299 and can be bought from their [official store][35].

Key Specifications

SoC: HiSilicon Kirin 970
CPU: ARM Cortex-A73 4-Core @ 2.36GHz and ARM Cortex-A53 4-Core @ 1.8GHz
GPU: ARM Mali-G72 MP12
RAM: 6 GB LPDDR4X @ 1866MHz
Storage: 64 GB UFS 2.1 microSD
Display: HDMI and 4 line MIPI/LCD port

6. Google Coral Dev Board

![][36]

The Coral Dev Board is Google’s first attempt at an SBC dedicated for edge computing. It is capable of performing high speed ML (Machine Learning) inferencing and has support for TensorFlow Lite and AutoML Vision Edge.

The board is priced at $129.99 and is available through [Coral’s official website][37].

Key Specifications

CPU: NXP i.MX 8M SoC (4-Core Cortex-A53, Cortex-M4F)
ML Accelerator: Google Edge TPU coprocessor
GPU: Integrated GC7000 Lite Graphics
RAM: 1 GB LPDDR4
Storage: 8 GB eMMC and microSD slot
Display: HDMI 2.0a, 39-pin FFC connector for MIPI-DSI display (4-lane) and 24-pin FFC connector for MIPI-CSI2 camera (4-lane)

7. Google Coral Dev Board Mini

![][38]

The Coral Dev Board Mini is the successor to the Coral Dev Board, it packs in more processing power into a smaller form factor and a lower price point of $99.99.

The Coral Dev Board Mini can be purchased from their [official web store][39].

Key Specifications

CPU: MediaTek 8167s SoC (4-core Arm Cortex-A35)
ML Accelerator: Google Edge TPU coprocessor
GPU: IMG PowerVR GE8300
RAM: 2 GB LPDDR3
Storage: 8 GB eMMC
Display: micro HDMI (1.4), 24-pin FFC connector for MIPI-CSI2 camera (4-lane) and 24-pin FFC connector for MIPI-DSI display (4-lane)

Preview	Product	Price
![Google Coral Dev Board Mini][40] ![Google Coral Dev Board Mini][40]	[Google Coral Dev Board Mini][41]	$99.99[][10]	[Buy on Amazon][42]

Closing Thoughts

There is an SBC available in every price range for edge compute applications. Some are just basic, like the Nvidia Jetson Nano or the BeagleBone AI and some are performance oriented models like the BeagleV and Nvidia Jetson AGX Xavier.

If you are looking for something more universal you can check our article on Raspberry Pi alternatives that could help you in finding a suitable SBC for your use case.

If I missed any SBC dedicated for edge compute, feel free to let me know in the comments below.

Author info: Sourav Rudra is a FOSS Enthusiast with love for Gaming Rigs/Workstation building.

via: https://itsfoss.com/best-sbc-for-ai/

作者：Community 选题：lujun9972 译者：译者ID 校对：校对者ID

本文由 LCTT 原创编译，Linux中国荣誉推出

14 KiB Raw Blame History Unescape Escape

Best Single Board Computers for AI and Deep Learning Projects

1. Nvidia Jetson Family

Nvidia Jetson Nano Developer Kit

Nvidia Jetson Xavier NX Developer Kit

Nvidia Jetson AGX Xavier Developer Kit

2. ROCK Pi N10

3. BeagleBone AI

4. BeagleV

5. HiKey970

6. Google Coral Dev Board

7. Google Coral Dev Board Mini

Closing Thoughts

14 KiB

Raw Blame History