Market research estimates there will be as many as 20 billion connected devices in the market by 2020. These devices are expected to generate billions of petabytes of data traffic between cloud and edge devices. In 2017 alone, there were as many as 8.4B connected devices, highlighting the need to preprocess data at the edge. This has led many IoT device manufacturers, especially those working on vision-based devices like smart cameras, drones, robots, and AR/VR, to bring intelligence to the edge.
Through the recent addition of the Movidius VPU technology to its existing AI edge solutions portfolio, Intel is well positioned to provide solutions to help developers and data scientists pioneer the low-power intelligent edge devices segment. Ashwin Vijayakumar gives you a hands-on overview of Intel’s Movidius Neural Compute Stick, a miniature deep learning hardware development platform that you can use to prototype, tune, and validate your AI programs (specifically deep neural networks).
Ashwin Vijayakumar is lead developer evangelist and an embedded systems architect working on robotics, IoT, and automotive electronics at Intel. A results-oriented hands-on engineering leader, an entrepreneur, and an innovator with extensive experience in bringing embedded products to market, Ashwin is passionate about deploying products and sustaining them at every stage of product development lifecycle. He is currently focused on the front and rear end of the cycle (i.e., requirements gathering, analysis, prototyping, deployment, training and sales support, and maintenance and technical support).
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