Nvidia promotes AI with cheap, pocket-size computer

March 21, 2019 //By Rich Pell
Nvidia $99 AI computer Jetson Nano devkit module
Nvidia (San Jose, CA) has announced a pocket-size AI computer that, says the company, makes it possible to create millions of intelligent systems.

The CUDA-X-based Jetson Nano AI computer delivers 472 GFLOPS of compute performance for running modern AI workloads and consumes as little as 5 W. The Jetson Nano comes in two versions: a $99 devkit for developers, makers, and enthusiasts; and a $129 production-ready module for companies looking to create mass-market edge systems.

"Jetson Nano makes AI more accessible to everyone - and is supported by the same underlying architecture and software that powers our nation's supercomputers," says Deepu Talla, vice president and general manager of Autonomous Machines at NVIDIA. "Bringing AI to the maker movement opens up a whole new world of innovation, inspiring people to create the next big thing."

Jetson Nano supports high-resolution sensors, can process many sensors in parallel, and can run multiple modern neural networks on each sensor stream. It also supports many popular AI frameworks, allowing developers to integrate their preferred models and frameworks into the product.

The low-cost Jetson Nano Developer Kit , says the company, enables users to build AI projects that weren't previously possible and take existing projects to the next level - mobile robots and drones, digital assistants, automated appliances, and more. The kit comes with out-of-the-box support for full desktop Linux, compatibility with many popular peripherals and accessories, and ready-to-use projects and tutorials that help makers get started with AI quickly.

The Jetson Nano module , says the company, brings to life a new world of embedded applications, including network video recorders, home robots, and intelligent gateways with full analytics capabilities. It is designed to reduce overall development time and bring products to market faster by reducing the time spent in hardware design, test, and verification of a complex, robust, power-efficient AI system.

The design comes complete with power management, clocking, memory, and fully accessible input/outputs. Because the AI workloads are entirely software defined, companies can update performance and capabilities even after the system has been deployed.

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