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
“Exploiting Reduced Precision for Machine Learning on FPGAs,” a Presentation from Xilinx Read More +
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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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
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
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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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
Ryan Johnson, lead engineer at Twisthink, presents the “Optimize Performance: Start Your Algorithm Development With the Imaging Subsystem” tutorial at the May 2018 Embedded Vision Summit. Image sensor and algorithm performance are rapidly increasing, and software and hardware development tools are making embedded vision systems easier to develop. Even with these advancements, optimizing vision-based detection
Peter Corcoran, co-founder of FotoNation (now a core business unit of Xperi) and lead principle investigator and director of C3Imaging (a research partnership between Xperi and the National University of Ireland, Galway), presents the “Getting More from Your Datasets: Data Augmentation, Annotation and Generative Techniques” tutorial at the May 2018 Embedded Vision Summit. Deep learning
Hoon Choi, Senior Director of Design Engineering at Lattice Semiconductor, presents the “Deep Quantization for Energy Efficient Inference at the Edge” tutorial at the May 2018 Embedded Vision Summit. Intelligence at the edge is different from intelligence in the cloud in terms of requirements for energy, cost, accuracy and latency. Due to limits on battery
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