![]() ![]() Bringing AI to the maker movement opens up a whole new world of innovation, inspiring people to create the next big thing.” said Deepu Talla, VP and GM of Autonomous Machines at NVIDIA. “Jetson Nano makes AI more accessible to everyone - and is supported by the same underlying architecture and software that powers our nation’s supercomputer. It also has support for NVIDIA’s JetPack and DeepStream SDKs, same as the more expensive TX2 and AGX Boards. NVIDIA Maxwell™ architecture with 128 NVIDIA CUDA® coresġ2 lanes (3×4 or 4×2) MIPI CSI-2 DPHY 1.1 (1.5 Gbps)ġ x1/2/4 PCIE, 1x SDIO / 2x SPI / 6x I2C / 2x I2S / GPIOsĪlong with good hardware, you get support for the majority of popular AI frameworks like TensorFlow, PyTorch, Keras, etc. Quad-core ARM® Cortex®-A57 MPCore processor Full range of specifications can be found here. The port selection is also pretty decent with the Nano having Gigabit Ethernet, MIPI Camera, Display outputs, and a couple of USB ports (1×3.0, 3×2.0). NVIDIA Jetson Nano Specificationsįor $99, you get 472 GFLOPS of processing power due to 128 NVIDIA Maxwell Architecture CUDA Cores, a quad-core ARM A57 processor, 4GB of LP-DDR4 RAM, 16GB of on-board storage, and 4k video encode/decode capabilities. In other words, the Jetson Nano is powered by Ubuntu Linux. NVIDIA’s JetPack SDK provides a ‘complete desktop Linux environment based on Ubuntu 18.04 LTS’. Jetson Nano focuses on smaller AI projects It is also more secure since your data always stays on the device itself. This is great for privacy as well as saving on internet bandwidth. When connected to your system, it will be able to supply the processing power needed for Machine Learning and AI tasks without having to rely on the cloud. The Jetson Nano, is NVIDIA’s latest offering in this market. Products like the SparkFun Edge and OpenMV Board are good examples. Bring up Etcher tool and select the target SD card to which you want to flash the image. Now at the embedded level, products which could do such complex calculations required for AI and Machine Learning were sparse, but we’re seeing a great explosion these days in this product segment. Recently, there’s been a trend in shifting this computation away from the cloud and do it locally. Traditionally AI computing was always done in the cloud, where there was plenty of processing power available. In the last few years, we have seen a lot of advances in AI research. The Jetson Nano Development Kit (left) and the Jetson Nano Module (right) Bringing back AI development from ‘cloud’ ![]()
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