CIOTechie_ArtificialIntelligence_NVIDIA_Jetson-Nano_Developer_Edge-AI_Embedded-Device

Nvidia Introduces Jetson Nano For Developers To Implement Edge AI On Embedded Devices

Artificial Intelligence News

Nvidia_Introduces-Jetson-Nano-For-Developers-To-Implement-Edge-AI-On-Embedded-Devices Nvidia Introduces Jetson Nano For Developers To Implement Edge AI On Embedded DevicesComputer game company Nvidia has launched a new embedded computer to its Jetson line, named Jetson Nano, for developers to deploy Artificial Intelligence on the edge. The Jetson Nano, a CUDA-X computer delivers 472 GFLOP of computer power, 4GB of memory, and can operate on 5 watts of power. As the company’s latest and cheapest solution for edge AI, Jetson Nano for deploying AI on the edge without an internet connection follows the release of the Jetson AGX Xavier chip, which introduced last year, and Jetson TX2 launched in 2017.

The Jetson Nano developer kit is available for USD100, while users can get Jetson Minicomputers for embedding in hardware in production from June, with USD129. By comparison, the Xavier retails for USD 1,299 and TX2 for about USD600. Nvidia VP of autonomous machines Deepu Talla said they are bringing Jetson into the mainstream market. With technical aspect, the Jetson TX2 runs on 7.5-watts of power and 8GB of memory, while the AGX Xavier can run on as low as 10 watts of power and comes with 32GB of memory. Like its predecessors, the Jetson Nano will be capable of working with Nvidia’s more than 40 CUDA-X AI Deep Learning libraries. As per the reports, the Jetson system for edge computing on mobile or embedded devices is currently utilized by 200,000 developers. Edge computing assists powering inference for robots, drones, security cameras, and many other devices that don’t want to rely on an internet connection to work.

On the other side, Amazon’s AWS also announced the launch of instances powered by Tesla T4 GPUs, general availability for self-driving platform Constellation, the debut of the Safety Force Field for driverless vehicles, and the reorganization of more than 40 Nvidia Deep Learning acceleration libraries under a new umbrella, CUDA-X AI.