Summit 2019

“Dynamically Reconfigurable Processor Technology for Vision Processing,” a Presentation from Renesas

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Yoshio Sato, Senior Product Marketing Manager in the Industrial Business Unit at Renesas, presents the “Dynamically Reconfigurable Processor Technology for Vision Processing” tutorial at the May 2019 Embedded Vision Summit. The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially pipelined, time-multiplexed, reconfigurable- hardware compute resources. […]

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

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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

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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

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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

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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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“Training Data for Your CNN: What You Need and How to Get It,” a Presentation from Aquifi

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Carlo Dal Mutto, CTO of Aquifi, presents the “Training Data for Your CNN: What You Need and How to Get It” tutorial at the May 2019 Embedded Vision Summit. A fundamental building block for AI development is the development of a proper training set to allow effective training of neural nets. Developing such a training

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

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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

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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

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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

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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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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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