Edge AI and Vision Alliance

May 2016 Embedded Vision Summit Introductory Presentation (Day 1)

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Jeff Bier, Founder of the Embedded Vision Alliance, welcomes attendees to the May 2016 Embedded Vision Summit on May 2, 2016 (Day 1). Bier provides an overview of the embedded vision market opportunity, challenges, solutions and trends. He also introduces the Embedded Vision Alliance and the resources it offers for both product creators and potential […]

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Embedded Vision Insights: August 2, 2016 Edition

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FEATURED VIDEOS Combining Flexibility and Low-Power in Embedded Vision Subsystems: An Application to Pedestrian Detection Bruno Lavigueur, Embedded Vision Subsystem Project Leader at Synopsys, presents a case study of a pedestrian detection application. Starting from a high-level functional description in OpenCV, he decomposes and maps the application onto a heterogeneous platform consisting of a high-performance

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“Should Visual Intelligence Reside in the Cloud or at the Edge? Trade-offs in Privacy, Security and Performance,” a Presentation from Silk Labs

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Andreas Gal, CEO of Silk Labs, presents the "Should Visual Intelligence Reside in the Cloud or at the Edge? Trade-offs in Privacy, Security and Performance" tutorial at the May 2016 Embedded Vision Summit. The Internet of Things continues to expand and develop, including more intelligent connected devices that respond to people’s needs and alert them

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Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations

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Neural networks enable the identification of objects in still and video images with impressive speed and accuracy after an initial training phase. This so-called "deep learning" has been enabled by the combination of the evolution of traditional neural network techniques, with one latest-incarnation example known as a CNN (convolutional neural network), by the steadily increasing

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Computer Vision as the New Industry Growth Driver? Perspectives from the Embedded Vision Summit Investor Panel

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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

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Embedded Vision Insights: July 19, 2016 Edition

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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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“Image Sensors for Vision: Foundations and Trends,” a Presentation from ON Semiconductor

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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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Embedded Vision Insights: July 7, 2016 Edition

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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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“Real-world Vision Systems Design: Challenges and Techniques,” a Presentation from Intel

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Yury Gorbachev, Principal Engineer at Itseez (now part of Intel), presents the "Real-world Vision Systems Design: Challenges and Techniques" tutorial at the May 2016 Embedded Vision Summit. Computer vision is central to many modern, cool products and technologies, including augmented reality, virtual reality and drones. Thanks to recent advances in system-on-chip and embedded systems design,

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“TensorFlow: Enabling Mobile and Embedded Machine Intelligence,” a Presentation from Google

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Pete Warden, Research Engineer at Google, presents the "TensorFlow: Enabling Mobile and Embedded Machine Intelligence" tutorial at the May 2016 Embedded Vision Summit. Following a brief overview of the advances in deep learning and AI over the last few years, Pete discusses how Google uses TensorFlow to deploy those advances in products on mobile and

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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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