Jetson Nano Developer Kit - Getting Started with the NVIDIA Jetson Nano
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 Published On Jun 1, 2019

Today we will look at the NVIDIA Jetson Nano Developer Kit, a low-cost platform for developing Artificial Intelligence (AI) applications. I’ll show you how to get started with the Jetson Nano.

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The NVIDIA Jetson Developer Kit was introduced in late 2018 and became available in early 2019. It’s an inexpensive product with impressive capabilities.

The Jetson Nano Development Kit is part of the NVIDIA Jetson line of Artificial Intelligence development platforms. While most of the Jetson platforms are quite expensive the Nano kit is priced at only $99 USD, bringing the power of the Jetson platform to students, experimenters, makers, and independent developers.

The NVIDIA Jetson Nano Developer kit contains a Jetson Nano module mounted on a carrier board. The carrier board has 4 USB3 ports, a Gigabit Ethernet connector and both HDMI and DisplayPort video connectors. There is also a 40-pin GPIO connector that is Raspberry Pi GPIO compatible.

The Jetson Module is a card with a huge heatsink and a 260-pin SODIMM connector. It has a Quad-core ARM A57 CPU running at 1.43 GHz, a 128-core Maxwell GPU, and 4GB of ultra-fast memory.

In the Jetson Nano Developer Kit, the module has a microSD card, there is also a production version of the module that uses NVRAM.

As this is a developer kit the idea is that you would develop your software on a microSD card and then burn it onto NVRAM in your production design. Your design would use a 260-pin SODIMM connector for the module, bringing the power of Jetson to your own products.

I’ll show you how to get an image for Ubuntu Linux 18.04 configured with the NVIDIA JetPack SDK. We’ll burn the image onto a microSD card and use it to set up the Jetson.

Once we have it set up we will run a few CUDA parallel processing demos.

Here is what is in today's video:

00:00 - Introduction
02:04 - Introducing NVIDIA Jetson Nano
10:23 - Getting the microSD Image
12:16 - Installing Ubuntu and Jetpack SDK
16:05 - CUDA Parallel Processing Demos

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