Jetson Nano Nvidia

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

Source: NVIDIA

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

GPU128-core Maxwell
CPUQuad-core ARM A57 @ 1.43 GHz
Memory4 GB 64-bit LPDDR4 25.6 GB/s
StoragemicroSD (not included)
Video Encode4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
Video Decode4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265)
Camera1x MIPI CSI-2 DPHY lanes
ConnectivityGigabit Ethernet, M.2 Key E
DisplayHDMI 2.0 and eDP 1.4
USB4x USB 3.0, USB 2.0 Micro-B
OthersGPIO, I2C, I2S, SPI, UART
Mechanical69 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

Lets Plug and Play

⚠️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.

Comparison stats

Source: NVIDIA

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