Tom Wilson, Vice President of Automotive at Graphcore, presents the “DNN Challenges and Approaches for L4/L5 Autonomous Vehicles” tutorial at the May 2019 Embedded Vision Summit. The industry has made great strides in development of L4/L5 autonomous vehicles, but what’s available today falls far short of expectations set as recently as two to three years […]
Robert Lay, Computer Vision and Camera Product Manager at Qualcomm, presents the “Snapdragon Hybrid Computer Vision/Deep Learning Architecture for Imaging Applications” tutorial at the May 2019 Embedded Vision Summit. Advances in imaging quality and features are accelerating, thanks to hybrid approaches that combine classical computer vision and deep learning algorithms and that take advantage of
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.
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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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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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
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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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
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,
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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