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The Pace of Deep Learning and Artificial Intelligence On Edge Devices Reaches New Level With Advanced Intel Vision Accelerator Solutions

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Shreepanjali Mod
Recently, Intel revealed its new family of Intel Vision Accelerator Design Products aimed at analytics performance and AI (Artificial Intelligence) interference over edge devices, where data is generated and acted upon. These new acceleration solutions can be attained in two forms, first is the one that is built on high-performance INtel Arria 10 FPGA while the other one features a wide array of Intel Movidius™ vision processors. The accelerator solutions that are built on OpenVINO™ software toolkit that offers the developers with much better neural network performance on complete range of Intel products and also aids in real-time, cost-effective image analysis and intelligence inside their IoT (Internet of Things) devices.
 
New-Intel-Vision-Accelerator-Solutions-Speed-Deep-Learning-and-Artificial-Intelligence-on-Edge-Devices
 
Fig. 1: Representational Image of Intel Vision Accelerator Solutions
 
Jonathan Ballon, the general manager and vice president of Internet of Things Group at Intel, further adds, “Until recently, businesses have been struggling to implement deep learning technology. For transportation, smart cities, healthcare, retail and manufacturing industries, it takes specialized expertise, a broad range of form factors and scalable solutions to make this happen. Intel's Vision Accelerator Design Products now offer businesses choice and flexibility to easily and affordably accelerate AI at the edge to drive real-time insights.” 
 
Now let’s talk about its importance. There is a rising need for intelligence in the edge devices. Since deep learning is quickly replacing more and more customary computer vision techniques, businesses are now able to get in rich data from digital video. With the help of Intel Vision Accelerator Design Products, businesses are able to implement vision-based AI systems to analyze and collect data right over edge devices for real-time decision-making. Much advanced edge computing abilities are helpful in cutting down costs and drive up newer revenues streams and better services.