Yury Gorbachev, Principal Engineer and the Lead Architect for the Computer Vision SDK at Intel, presents the “Enabling Cross-platform Deep Learning Applications with the Intel CV SDK” tutorial at the May 2018 Embedded Vision Summit. Intel offers a wide array of processors for computer vision and deep learning at the edge, including CPUs, GPUs, VPUs […]
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
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
“Project Trillium: A New Suite of Machine Learning IP,” a Presentation from Arm Read More +
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
“Pilot AI Vision Framework: From Doorbells to Defense,” a Presentation from Pilot AI Read More +
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
“Embedding Programmable DNNs in Low-Power SoCs,” a Presentation from Xperi Read More +
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.
Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596
