Jetson Nano Nvidia

by Austin Eldrin, August 4, 2019

What is jetson nano? What is jetson nano used for? How much computing power does jetson nano provide? How much does jetson nano cost?

Jetson Nano

NVIDIA Jetson Nano is an integrated system-on-module (SoM) and developer kit from the NVIDIA Jetson family, including an integrated 128-core Maxwell GPU, quad-core ARM A57 64-bit CPU, 4 GB LPDDR4 memory and MIPI CSI-2 and PCIe Gen2 high-speed I / O assistance.

Jetson Nano Developer Kit

NVIDIA ® Jetson NanoTM Developer Kit is a tiny, strong computer that allows you to run various parallel neural networks for apps such as classification of images, detection of objects, segmentation, and speech in simple word it is useful for deploying computer vision and deep learning. All in a user-friendly platform running in as few as 5 watts up to 10 watts.

Jetson nano technical specifiaction

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 \| 4x 1080p @ 30 \| 9x 720p @ 30 (H.264/H.265)
Video Decode	4K @ 60 \| 2x 4K @ 30 \| 8x 1080p @ 30 \| 18x 720p @ 30 (H.264/H.265)
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	69 mm x 45 mm, 260-pin edge connector

What’s Included in the box

80x100mm Carrier Board with jetson nano
A Passive heatsink
Pop-up paper stand
Getting Started Guide

Detail About development kit

*In a tiny package, Nvidia’s Jetson Nano packs quite a punch, including both the Nano module and a carrier board with lots of IO choices.*

Ports & Interfaces

4x USB 3.0 A (Host)
USB 2.0 Micro B (Device)
MIPI CSI-2 x2 (15-position Camera Flex Connector)
HDMI 2.0
DisplayPort
Gigabit Ethernet (RJ45)
M.2 Key-E with PCIe x1
MicroSD card slot
(3x) I2C, (2x) SPI, UART, I2S, GPIOs

Getting Started

⚠️Warning While the OS image and other installations fit comfortably on a 16 GB card, it generally results in root filesystem signing up and ‘ no space left on device ‘ errors during inferencing using that tiny card. So minimum 32GB is required.
Plug in display into Jetson via HDMI also attach USB keyboard & mouse, and apply power to boot it up.

Libraries and APIs

TensorRT and cuDNN for high-performance deep learning applications
CUDA for GPU accelerated applications across multiple domains
Multimedia API package for camera applications and sensor driver development
VisionWorks and OpenCV for visual computing applications

Example of testing

JetPack involves several samples showing the use of parts from JetPack. The examples are stored in the filesystem of the reference and can be recorded on the developer kit.