Mohammad Rastegari, CTO of Xnor.ai, presents the “At the Edge of AI At the Edge: Ultra-efficient AI on Low-power Compute Platforms” tutorial at the May 2018 Embedded Vision Summit. Improvements in deep learning models have increased the demand for AI in several domains. These models demand massive amounts of computation and memory, so current AI […]
Fergus Casey, R&D Director for ARC Processors at Synopsys, presents the “Designing Smarter, Safer Cars with Embedded Vision Using Synopsys EV Processor Cores” tutorial at the May 2018 Embedded Vision Summit. Consumers, the automotive industry and government regulators are requiring greater levels of automotive functional safety with each new generation of cars. Embedded vision, using
How does multispectral imaging work and what kind of vision applications can benefit from this technology? Multispectral cameras can be used for a variety of applications. Although traditionally primarily used in labs, they are now also used in drones, for example, to check the health of plants in the field. Watch Basler's latest Vision Campus
Basler’s Thies Möller Explains Multispectral Imaging Read More +
Tom Michiels, System Architect for Embedded Vision at Synopsys, presents the “New Deep Learning Techniques for Embedded Systems” tutorial at the May 2018 Embedded Vision Summit. In the past few years, the application domain of deep learning has rapidly expanded. Constant innovation has improved the accuracy and speed of learning and inference. Many techniques are
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Steve Steele, Director of Platforms in the Machine Learning Group at Arm, presents the “Project Trillium: A New Suite of Machine Learning IP from Arm” tutorial at the May 2018 Embedded Vision Summit. Machine learning processing engines today tend to focus on specific device classes or the needs of individual sectors. Arm’s Project Trillium changes
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Salil Raje, Senior Vice President in the Software and IP Products Group at Xilinx, presents the “Leveraging Edge and Cloud for Visual Intelligence Solutions” tutorial at the May 2018 Embedded Vision Summit. For many computer vision systems, a critical decision is whether to implement vision processing at the edge or in the cloud. In a
Friedrich Dierks, Director of Product Marketing and Development for the Module Business at Basler, presents the “Designing Vision Front Ends for Embedded Systems” tutorial at the May 2018 Embedded Vision Summit. This presentation guides viewers through the process of specifying and selecting a vision front end for an embedded system. It covers topics such as
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Nick Ni, Director of Product Marketing for AI and Edge Computing at Xilinx, presents the “Achieving 15 TOPS/s Equivalent Performance in Less Than 10 W Using Neural Network Pruning on Xilinx Zynq” tutorial at the May 2018 Embedded Vision Summit. Machine learning algorithms, such as convolution neural networks (CNNs), are fast becoming a critical part
Kees Vissers, Distinguished Engineer at Xilinx, presents the “Exploiting Reduced Precision for Machine Learning on FPGAs” tutorial at the May 2018 Embedded Vision Summit. Machine learning algorithms such as convolutional neural networks have become essential for embedded vision. Their implementation using floating-point computation requires significant compute and memory resources. Research over the last two years
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Arndt Bake, Chief Marketing Officer at Basler, presents the “Building up a Start-up in Embedded Vision: Lessons from Machine Vision” tutorial at the May 2018 Embedded Vision Summit. As embedded vision becomes more capable, it is proliferating into many new markets and attracting many new start-ups. How can you gauge when the time is right
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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