Processors

“Eye Tracking for the Future: The Eyes Have It,” a Presentation from Parallel Rules

Peter Milford, President of Parallel Rules, presents the “Eye Tracking for the Future: The Eyes Have It” tutorial at the May 2019 Embedded Vision Summit. Eye interaction technologies complement augmented and virtual reality head-mounted displays. In this presentation, Milford reviews eye tracking technology, concentrating mainly on camera-based solutions and associated system requirements. Wearable eye tracking […]

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“Fundamentals of Monocular SLAM,” a Presentation from Cadence

Shrinivas Gadkari, Design Engineering Director at Cadence, presents the “Fundamentals of Monocular SLAM” tutorial at the May 2019 Embedded Vision Summit. Simultaneous Localization and Mapping (SLAM) refers to a class of algorithms that enables a device with one or more cameras and/or other sensors to create an accurate map of its surroundings, to determine the

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“Fast and Accurate RMNet: A New Neural Network for Embedded Vision,” a Presentation from Intel

Ilya Krylov, Software Engineering Manager at Intel, presents the “Fast and Accurate RMNet: A New Neural Network for Embedded Vision” tutorial at the May 2019 Embedded Vision Summit. Usually, the top places in deep learning challenges are won by huge neural networks that require massive amounts of data and computation, making them impractical for use

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“Hardware-aware Deep Neural Network Design,” a Presentation from Facebook

Peter Vajda, Research Manager at Facebook, presents the “Hardware-aware Deep Neural Network Design” tutorial at the May 2019 Embedded Vision Summit. A central problem in the deployment of deep neural networks is maximizing accuracy within the compute performance constraints of embedded devices. In this talk, Vajda discusses approaches to addressing this challenge based on automated

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“Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling,” a Presentation from Mythic

Mike Henry, CEO and Founder of Mythic, presents the “Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling” tutorial at the May 2019 Embedded Vision Summit. AI inference at the edge will continue to create insatiable demand for compute performance in power- and cost-constrained form factors. Taking into account past trends,

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“The Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability,” a Presentation from Xilinx

Nick Ni, Director of Product Marketing at Xilinx, presents the “Xilinx AI Engine: High Performance with Future-proof Architecture Adaptability” tutorial at the May 2019 Embedded Vision Summit. AI inference demands orders- of-magnitude more compute capacity than what today’s SoCs offer. At the same time, neural network topologies are changing too quickly to be addressed by

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“Designing Your Next Vision Product Using a Systems Approach,” a Presentation from Teknique

Ben Bodley, CEO of Teknique, presents the “Designing Your Next Vision Product Using a Systems Approach,” tutorial at the May 2019 Embedded Vision Summit. Today it’s easier than ever to create a credible demo of a new smart camera product for a specific application. But the distance from a demo to a robust product is

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“Efficient Deployment of Quantized ML Models at the Edge Using Snapdragon SoCs,” a Presentation from Qualcomm

Felix Baum, Director of Product Management for AI Software at Qualcomm, presents the “Efficient Deployment of Quantized ML Models at the Edge Using Snapdragon SoCs” tutorial at the May 2019 Embedded Vision Summit. Increasingly, machine learning models are being deployed at the edge, and these models are getting bigger. As a result, we are hitting

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“Using Blockchain to Create Trusted Embedded Vision Systems,” a Presentation from Basler

Thies Möller, Technical Architect at Basler, presents the “Using Blockchain to Create Trusted Embedded Vision Systems” tutorial at the May 2019 Embedded Vision Summit. In many IoT architectures, sensor data must be passed to cloud services for further processing. Traditionally, “trusted third parties” have been used to secure this data. In this talk, Möller explores

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“Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit,” a Presentation from Au-Zone Technologies

Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, presents the “Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit” tutorial at the May 2019 Embedded Vision Summit. In this presentation, Taylor describes methods and tools for developing, profiling and optimizing neural network solutions for deployment on Arm MCUs, CPUs 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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PO Box #4446
Walnut Creek, CA 94596

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