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NVIDIA Jetson TX2 Enables AI at the Edge

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IoT Platform for Manufacturing, Industrial, Retail Made Possible by NVIDIA’s AI-Powered Jetson
 
SANTA CLARA, Calif.—March 7, 2017—NVIDIA today unveiled the NVIDIAŽ Jetson™ TX2,
a credit card-sized platform that delivers AI computing at the edge — opening the door to powerfully intelligent factory robots, commercial drones and smart cameras for AI cities.
 
Jetson TX2 offers twice the performance of its predecessor, or it can run at more than twice the power efficiency, while drawing less than 7.5 watts of power. This allows Jetson TX2 to run larger, deeper neural networks on edge devices. The result: smarter devices with higher accuracy and faster response times for tasks like image classification, navigation and speech recognition.
 
The Jetson TX2 joins the Jetson TX1 and TK1 products for embedded computing. Jetson is an open platform that is accessible to anyone for developing advanced AI solutions at the edge — from enterprise companies and startups to researchers and high school students.
 
Partner Support
NVIDIA Jetson is a powerful platform that enables Cisco to add AI features such as facial and speech recognition to its Cisco Spark products that connect everyone, everywhere. Cisco is able to drive new experiences and remove the barriers between physical and virtual spaces, thanks to Jetson TX2’s advanced technology capabilities in AI computing and graphics.

contemporary The Jetson family is supported by the most comprehensive SDK for AI computing, JetPack 3.0, which makes it easy to integrate AI into a wide variety of applications, and support the following: 
TensorRT™ 1.0, a high-performance neural network inference engine for production deployment of deep learning applications
cuDNN 5.1, a GPU-accelerated library of primitives for deep neural networks
VisionWorks™ 1.6, a software development package for computer vision and image processing
The latest graphics drivers and APIs, including OpenGL 4.5, OpenGL ES 3.2, EGL 1.4 and Vulkan 1.0
CUDAŽ 8, which turns the GPU into a general-purpose massively parallel processor, giving developers access to tremendous performance and power-efficiency
 
For more information visit http://www.nvidia.com












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