This article was originally published by Embedded Vision Alliance consultant Dave Tokic. It is reprinted here with Tokic's permission. It seems to me that hardly a day goes by without some mention of self-driving cars (mostly good, some tragic), drones that follow you to record your ski run, augmented and virtual reality goggles that immerse […]
FEATURED VIDEOS "Large-Scale Deep Learning for Building Intelligent Computer Systems," a Keynote Presentation from Google Jeff Dean, Senior Fellow at Google, presents the keynote talk, "Large-Scale Deep Learning for Building Intelligent Computer Systems," at the May 2016 Embedded Vision Summit. Over the past few years, Google has built two generations of large-scale computer systems for
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Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, presents the "Is Vision the New Wireless?" tutorial at the May 2016 Embedded Vision Summit. Over the past 20 years, digital wireless communications has become an essential technology for many industries, and a primary driver for the electronics industry. Today, computer vision is showing
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Robin Jenkin, Director of Analytics, Algorithm and Module Development at ON Semiconductor, presents the "Image Sensors for Vision: Foundations and Trends" tutorial at the May 2016 Embedded Vision Summit. Choosing the right sensor, lens and system configuration is crucial to setting you off in the right direction for your vision application. Jenkin examines fundamental considerations
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Gerrit Fischer, Head of Product Market Management at Basler, presents the "Understanding Camera Subsystems for Your Embedded Vision System and Making the Right Choice" tutorial at the May 2016 Embedded Vision Summit. More than ever, you have a wide range of camera subsystems to choose from. At one end of the spectrum, a system designer
Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Efficient Convolutional Neural Network Inference on Mobile GPUs" tutorial at the May 2016 Embedded Vision Summit. GPUs have become established as a key tool for training of deep learning algorithms. Deploying those algorithms on end devices is a key enabler to their commercial success and
Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit. While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance
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FEATURED VIDEOS "Challenges in Object Detection on Embedded Devices," a Presentation from CEVA As more products ship with integrated cameras, says Adar Paz, Imaging and Computer Vision Team Leader at CEVA, there is an increased potential for computer vision (CV) to enable innovation. For instance, CV can tackle the "scene understanding" problem by first figuring
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Yair Siegel, Director of Segment Marketing at CEVA, presents the "Fast Deployment of Low-power Deep Learning on CEVA Vision Processors" tutorial at the May 2016 Embedded Vision Summit. Image recognition capabilities enabled by deep learning are benefitting more and more applications, including automotive safety, surveillance and drones. This is driving a shift towards running neural
Tim Hartley, Product Manager in the Personal Mobile Compute Business Line at ARM, presents the "Lessons Learned from Bringing Mobile and Embedded Vision Products to Market" tutorial at the May 2016 Embedded Vision Summit. Great news: technology is finally at a point where we can build sophisticated computer vision applications that run on mass market
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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