Jetson Nano

NVIDIA Jetson Nano is an embedded 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, 4GB LPDDR4 memory, along with support for MIPI CSI-2 and PCIe Gen2 high-speed I/O.

Useful for deploying computer vision and deep learning, Jetson Nano runs Linux and provides 472 GFLOPS of FP16 compute performance with 5-10W of power consumption.

Jetson Nano is currently available as the Jetson Nano Developer Kit for $99, with the production compute module coming in June 2019. See the wiki of the other Jetson's here.

Technical Blog — NVIDIA Jetson Nano Brings AI to Everyone

Jetson Nano Developer Kit

The Jetson Nano Developer Kit is an easy way to get started using Jetson Nano, including the module, carrier board, and software. It costs $99 and is available from distributors worldwide.

What's Included

• 80x100mm Reference Carrier Board
• Jetson Nano Module with passive heatsink
• Pop-Up Stand
• Getting Started Guide

(the complete devkit with module and heatsink weighs 138 grams)

What You Will Need

• Power Supply
  • 5V⎓2A Micro-USB adapter (see Adafruit GEO151UB)
  • 5V⎓4A DC barrel jack adapter, 5.5mm OD x 2.1mm ID x 9.5mm length, center-positive (see Adafruit 1446)
  • See Power Supply Considerations for more information.
• MicroSD card (16GB UHS-1 recommended minimum)

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

• Follow the Getting Started with Jetson Nano Guide to setup your devkit and format the MicroSD card.
  
• Plug in an HDMI display into Jetson, attach a USB keyboard & mouse, and apply power to boot it up.
  
• Visit the Embedded Developer Zone and Jetson Nano Developer Forum to access the latest documentation & downloads.

Availability

The devkit is available for $99 from the NVIDIA webstore and global distributors, including:

• Amazon
• Arrow
• Newegg
• Seeed Studio
• Silicon Highway
• SparkFun

For the full list, refer to the Region Selector.

Software Support

• JetPack 4.2
• Linux4Tegra R32.1 (L4T)
• Linux kernel 4.9
• Ubuntu 18.04 LTS aarch64
• CUDA Toolkit 10.0
• cuDNN 7.3.1
• TensorRT 5.0.6
• TensorFlow 1.31.1
• VisionWorks 1.6
• OpenCV 3.3.1
• OpenGL 4.6
• OpenGL ES 3.2
• EGL 1.5
• Vulkan 1.1
• GStreamer 1.14.1
• V4L2 media controller support

Guides and Tutorials

This section contains recipes for following along on Jetson Nano.

System Tools

• L4T Kernel Development Guide
• Power Supply Considerations
• Upstream Development Guide
• CUDA and VisionWorks Samples
• Preliminary 3D CAD Model
• Mounting a SWAP File
• GPIO Header Pin-out
• Reading Serial Number
• jetson_easy - automatic setup/scripting
• jetson_stats - jtop, service and other tools

Multimedia

• RidgeRun's GstInterpipe (GStreamer plug-in for communication between two or more independent pipelines)
• RidgeRun's GstRRWebRTC (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)
• RidgeRun's GstRTSPSink (GStreamer element for high performance streaming to multiple computers using the RTSP/RTP protocols)
• RidgeRun's Gstreamer Daemon - GstD (GStreamer framework for controlling audio and video streaming using TCP connection messages)
• RidgeRun's GstCUDA (RidgeRun CUDA ZeroCopy for GStreamer)
• RidgerRun's GstPTZR (GStreamer Pan Tilt Zoom and Rotate Element)
• RidgeRun's GstColorTransfer (GStreamer plug-in that transfers the color scheme from a reference to a target image)

Deep Learning

• Hello AI World (jetson-inference)
• TensorFlow 1.13.1 Installer (pip wheel)
• PyTorch 1.1 Installer (pip wheel)
• MXNet 1.4 Installer (pip wheel)
• Deep Learning Inference Benchmarking Instructions
• TensorFlow Object Detection With TensorRT (TF-TRT)
• RidgeRun's GstInference
• RidgeRun's R2Inference

See the NVIDIA AI-IoT GitHub for other coding resources on deploying AI and deep learning.

Robotics

• NVIDIA JetBot (AI-powered robotics kit)
• jetbot_ros (ROS nodes for JetBot)
• ROS Melodic (ROS install guide)
• ros_deep_learning (jetson-inference nodes)

Ecosystem Products and Sensors

The following are 3rd-party accessories, peripherals, and cameras available for Jetson Nano.

Cameras

• e-con Systems e-CAM30_CUNANO (3.4 MP MIPI Camera)
• Logitech C920 (USB webcam)
• Leopard Imaging LI-IMX219-MIPI-FF-NANO (IMX219 sensor)
• Raspberry Pi Camera v2 (IMX219 sensor)
• Stereolabs ZED (stereo camera)

Carriers

• Antmicro Jetson Nano Baseboard (module carrier)
• Auvidea JN30 (module carrier)
• Auvidea JN30-LC (module carrier)

Enclosures

• ConnectTech Nano-Pac (3D-printable enclosure)
• Jetson Nano Case (3D-printable enclosure)
• Jetson NanoMesh (3D-printable enclosure)
• Jetson NanoMesh Mini (3D-printable enclosure)
• jetson_nano_enc (3D-printable enclosure)
• Geekworm Jetson Nano Case (metal enclosure)

Power Supplies

See the Power Supply section for more information about selecting proper power adapters.

• Adafruit GEO151UB (5V⎓2.5A Micro-USB adapter)
• Adafruit 1446 (5V⎓4A DC barrel jack adapter)

Battery Packs

• INUI 10000mAh (dual 5V⎓3A Micro-USB)
• Krisdonia 25000mAh (5V⎓3A Micro-USB / DC barrel jack)

Wireless

• Edimax EW-7811Un (USB Wi-Fi wireless dongle)
• Intel 8265NGW (M.2 Key-E Wi-Fi/BT wireless card)

Other

• M.2 Key-E to Mini-PCIe (PCIe adapter)
• M.2 Key-E to Key-M (PCIe adapter)
• Noctua NF-A4x20 5V PWM (optional fan)

See the Jetson Nano Supported Components List for devices that have been qualified by NVIDIA to work with Jetson Nano.

Getting Help

If you have a technical question or bug report, please visit the Jetson Nano Developer Forum and search or start a new topic.

See the official Support page on Embedded Developer Zone for warranty and RMA information.

For NVIDIA webstore Customer Service, please see the My Account page or contact 1-800-797-6530.