Seema Mirchandaney, Engineering Manager for Software Tools at Synopsys, presents the "Using the OpenCL C Kernel Language for Embedded Vision Processors" tutorial at the May 2016 Embedded Vision Summit. OpenCL C is a programming language that is used to write computation kernels. It is based on C99 and extended to support features such as multiple […]
Neural networks enable the identification of objects in still and video images with impressive speed and accuracy after an initial training phase. This so-called "deep learning" has been enabled by the combination of the evolution of traditional neural network techniques, with one latest-incarnation example known as a CNN (convolutional neural network), by the steadily increasing
Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations Read More +
Pierre Paulin, Senior R&D Director for Embedded Vision at Synopsys, presents the "Programming Embedded Vision Processors Using OpenVX" tutorial at the May 2016 Embedded Vision Summit. OpenVX, a new Khronos standard for embedded computer vision processing, defines a higher level of abstraction for algorithm specification, with the goal of enabling platform and tool innovation in
“Programming Embedded Vision Processors Using OpenVX,” a Presentation from Synopsys Read More +
Pierre Paulin, R&D Director for Embedded Vision at Synopsys, presents the "Low-power Embedded Vision: A Face Tracker Case Study" tutorial at the May 2015 Embedded Vision Summit. The ability to reliably detect and track individual objects or people has numerous applications, for example in the video-surveillance and home entertainment fields. While this has proven to
Bruno Lavigueur, Project Leader for Embedded Vision at Synopsys, presents the "Tailoring Convolutional Neural Networks for Low-Cost, Low-Power Implementation" tutorial at the May 2015 Embedded Vision Summit. Deep learning-based object detection using convolutional neural networks (CNN) has recently emerged as one of the leading approaches for achieving state-of-the-art detection accuracy for a wide range of
Bruno Lavigueur, Embedded Vision Subsystem Project Leader at Synopsys, presents the "Combining Flexibility and Low-Power in Embedded Vision Subsystems: An Application to Pedestrian Detection" tutorial at the May 2014 Embedded Vision Summit. Lavigueur presents an embedded-mapping and refinement case study of a pedestrian detection application. Starting from a high-level functional description in OpenCV, he decomposes
This article was originally published at John Day's Automotive Electronics News. It is reprinted here with the permission of JHDay Communications. Thanks to the emergence of increasingly capable and cost-effective processors, image sensors, memories and other semiconductor devices, along with robust algorithms, it's now practical to incorporate computer vision into a wide range of embedded
Tom Michiels, R&D Manager at Synopsys, presents the "Designing a Multi-Core Architecture Tailored for Pedestrian Detection Algorithms" tutorial within the "Algorithms and Implementations" technical session at the October 2013 Embedded Vision Summit East. Pedestrian detection is an important function in a wide range of applications, including automotive safety systems, mobile applications, and industrial automation. A
Following up on a recent news posting regarding an upcoming Japan-based event, Embedded Vision Alliance member company Synopsys also has a U.S.-based seminar coming up in the near future. On Wednesday, May 29, from 9:30am – 2:30pm in Mountain View, California, the company will present a seminar exploring application-specific processor (ASIP) design and prototyping. ASIPs,
Markus Wloka, R&D Director for System-Level Solutions at Synopsys, presents the "Lessons Learned: FPGA Prototyping of a Processor-Based Embedded Vision Application" tutorial within the "Developing Vision Software, Accelerators and Systems" technical session at the April 2013 Embedded Vision Summit. This presentation covers the steps of building a programmable vision system and highlights the importance of
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