Algorithms

“Project Trillium: A New Suite of Machine Learning IP,” a Presentation from Arm

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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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“Leveraging Edge and Cloud for Visual Intelligence Solutions,” a Presentation from Xilinx

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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

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“Designing Vision Front Ends for Embedded Systems,” a Presentation from Basler

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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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“Achieving 15 TOPS/s Equivalent Performance in Less Than 10 W Using Neural Network Pruning,” a Presentation from Xilinx

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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

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“Exploiting Reduced Precision for Machine Learning on FPGAs,” a Presentation from Xilinx

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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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“Pilot AI Vision Framework: From Doorbells to Defense,” a Presentation from Pilot AI

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Jonathan Su, CEO of Pilot AI, presents the “Pilot AI Vision Framework: From Doorbells to Defense” tutorial at the May 2018 Embedded Vision Summit. Pilot AI’s Vision Framework has enabled real-time detection, classification and tracking in thousands of devices, from consumer applications to federal contracts. Though diverse in end-user application, these use cases all share

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“Optimizing Your System Software and BSP for Embedded Vision and AI,” a Presentation from Thundersoft

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Daniel Sun, Vice President of the Intelligent Vision Business Unit at Thundersoft, presents the “Optimizing Your System Software and BSP for Embedded Vision and AI” tutorial at the May 2018 Embedded Vision Summit. While computer vision and AI algorithms tend to get the most attention, many other software components can have an equally important impact

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“Deep Learning in MATLAB: From Concept to Optimized Embedded Code,” a Presentation from MathWorks

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Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, present the “Deep Learning in MATLAB: From Concept to Optimized Embedded Code” tutorial at the May 2018 Embedded Vision Summit. In this presentation, you’ll learn how to adopt MATLAB to design deep learning based vision applications and re-target

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“Embedding Programmable DNNs in Low-Power SoCs,” a Presentation from Xperi

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Steve Teig, Chief Technology Officer at Xperi, presents the “Embedding Programmable DNNs in Low-Power SoCs” tutorial at the May 2018 Embedded Vision Summit. This talk presents the latest generation of FotoNation’s (a core business unit of Xperi) Image Processing Unit (IPU)—an embedded AI enabled image processing engine that can be customized and adapted to suit

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“Creating a Computationally Efficient Embedded CNN Face Recognizer,” a Presentation from PathPartner Technology

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Praveen G.B., Technical Lead at PathPartner Technology, presents the “Creating a Computationally Efficient Embedded CNN Face Recognizer” tutorial at the May 2018 Embedded Vision Summit. Face recognition systems have made great progress thanks to availability of data, deep learning algorithms and better image sensors. Face recognition systems should be tolerant of variations in illumination, pose

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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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