The Jetson Nano is a Linux SBC with AI hardware for DIY Artificial Intelligence projects

; Date: May 14, 2019

Tags: Jetson Nano »»»» Linux Single Board Computers »»»» DIY AI Hardware

The Jetson NANO board is a Linux Single Board computer, packaged with GPIO pins and other things meant to be attractive to DIY hardware hackers. While the main CPU is a Quad-core ARM A57 @ 1.43 GHz, what makes this interesting is the 128-core Maxwell GPU by Nvidia. Nvidia is the manufacturer of this board, and the GPU is there to support experimenters developing GPU-based artificial intelligence software.

Let's start with the technical data:

Technical Specifications Data
GPU 128-core Maxwell
CPU Quad-core ARM A57 @ 1.43 GHz
Memory 4 GB 64-bit LPDDR4 25.6 GB/s
Storage microSD (not included)
Video Encode 4K @ 30
Video Decode 4K @ 60
Camera 1x MIPI CSI-2 DPHY lanes
Connectivity Gigabit Ethernet, M.2 Key E
Display HDMI 2.0 and eDP 1.4
USB 4x USB 3.0, USB 2.0 Micro-B
Others GPIO, I2C, I2S, SPI, UART
Mechanical 100 mm x 80 mm x 29 mm

For a Single Board Computer the CPU and memory are good, but not the highest. Having 4GB of memory means this board will be able to run some demanding software. It has a good selection of USB ports and Gigabit ethernet, but no on-board WiFi. The hardware I/O is very useful for integration into a device like a robot.

The primary focus of this is the GPU, which is decidedly high end.

What Nvidia says is:

NVIDIA Jetson Nano Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.

While this can run many different operating systems, NVIDIA pushes a version of Ubuntu. With that OS you can access a large variety of AI software that can be easily configured to utilize the GPU.

About the Author(s)

David Herron : David Herron is a writer and software engineer focusing on the wise use of technology. He is especially interested in clean energy technologies like solar power, wind power, and electric cars. David worked for nearly 30 years in Silicon Valley on software ranging from electronic mail systems, to video streaming, to the Java programming language, and has published several books on Node.js programming and electric vehicles.