The Arduino development board , in its various versions, is a great option for makers and developers who want to create their own DIY projects. On the other hand, there's also the Raspberry Pi , a small and inexpensive single-board computer (SBC) for creating a multitude of projects. Added to all of these is the enormous number of alternatives available on the market. But you're probably looking for something more specific, something to work with neural networks and AI . Then the NVIDIA Jetson Nano board is what you're looking for.
If you're planning to develop projects based on neural networks or learn about them, you can count on this NVIDIA Jetson Nano board . All for just over €100, not too expensive considering the prices of other smart systems…
What is Jetson?
NVIDIA Jetson Nano is a project from the well-known graphics chip company that enables the development of many new, small-sized AI systems. Furthermore, compared to the prices of other neural network projects, it does so at a significantly lower cost and with lower power consumption.
With this development board you can create a multitude of projects , such as IoT applications, from small domestic robots to other intelligent systems you can think of, including network video recorders (NVRs), smart gateways, etc.
All of this is contained in a small PCB module measuring approximately 70x45mm in its Nano version, the smallest size. In fact, it's a ready-to-use SOM (System on Module) module , along with the development kit.
Technical characteristics
As for the technical specifications of the NVIDIA Jetson Nano, you'll find a powerful card capable of delivering 472 GFLOPS of performance to run modern AI algorithms very quickly. It could even run multiple neural networks in parallel and process several high-resolution sensors simultaneously.
And all this with remarkably efficient energy consumption. Despite its power, it only consumes between 5 and 10 watts . A very low power consumption considering the features it offers.
For more details, here is the complete specifications table :
| GPU | NVIDIA Maxwell architecture™ with 128 NVIDIA CUDA cores® | |
| CPU | ARM processor® Cortex®-A57 MPCore Quad Core | |
| Conference proceedings | 4GB 4-bit LPDDR64 | |
| Storage | 16GB eMMC 5.1 Flash storage | |
| Video encoding | 4K 30 frames (H.264 / H.265) | |
| Video decoding | 4K 60 frames (H.264 / H.265) | |
| Camera | 12-way (3 x 4 or 4 x 2) MIPI CSI-2 DPHY 1.1 (18 Gbps) | |
| Connectivity | Gigabit Ethernet | |
| Screen | HDMI 2.0 or DP 1.2 | eDP 1.4 | DSI (1 x 2) 2 simultaneous | |
| UPHY | 1 1/2/4 PCIE, 1 USB 3.0, 3 USB 2.0 | |
| IS | 1 SDIO / 2 SPI / 4 I2C / 2 I2S / GPIO | |
| Size | 69,6 mm x 45 mm | |
| Mechanics | 260 pin connector | |
Other similar products
NVIDIA also offers other products similar to Jetson Nano for developing AI with artificial neural networks. Some examples are:
- No products found.: a SOM module that offers all the power of a supercomputer with very small dimensions. You can get up to 21 TOPs, that is, 21 Tera Operations per seconds. More than enough power to run modern neural networks in parallel and process data from multiple high-resolution sensors at the same time.
- No products found.: a new module that marks a milestone in terms of computational density and efficiency. For AI, allowing the creation of new generations of intelligent machines.
- No products found.- Another high-speed, energy-efficient development board for embedded AI computing. A supercomputer in a module based on the NVIDIA Pascal GPU. With up to 8GB of RAM and a bandwidth of 59,7GB / s.
However, as you will see, its older siblings have considerably higher prices …
Acquire the NVIDIA Jetson Nano
If you decide to buy the NVIDIA Jetson Nano , you have several options. One is the products offered through the Amazon platform. You'll find either the development board alone, or more complete development kits that include the power adapter, etc. For example:
- No products found.
- No products found.
- Buy only the SOM module
Remember that machine learning , artificial intelligence, deep learning, and similar technologies are becoming increasingly popular due to their numerous exciting applications. Therefore, learning about them could be beneficial for developing new projects in the future or securing interesting jobs in companies that require these skills.