Brian Dipert

“Dataflow: Where Power Budgets Are Won and Lost,” a Presentation from Movidius

Algorithms & Models, Intel, Processors, Software, Tools, Videos /

Sofiane Yous, Principal Scientist in the machine intelligence group at Movidius, presents the "Dataflow: Where Power Budgets Are Won and Lost" tutorial at the May 2016 Embedded Vision Summit. This presentation showcases stories from the front lines in the battle between power and performance in embedded vision, deep learning and computational imaging applications. First, Youse […]

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May 2016 Embedded Vision Summit Introductory Presentation (Day 1)

Edge AI and Vision Alliance, Videos /

Jeff Bier, Founder of the Embedded Vision Alliance, welcomes attendees to the May 2016 Embedded Vision Summit on May 2, 2016 (Day 1). Bier provides an overview of the embedded vision market opportunity, challenges, solutions and trends. He also introduces the Embedded Vision Alliance and the resources it offers for both product creators and potential

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“Using the OpenCL C Kernel Language for Embedded Vision Processors,” a Presentation from Synopsys

Algorithms & Models, Processors, Software, Synopsys, Tools, Videos /

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

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ceva

Rockchip and CEVA Extend Partnership to Include CEVA-XM4 Intelligent Vision DSP

News /

CEVA’s 4th generation imaging and vision DSP adds low-power image enhancement and computer vision capabilities to Rockchip’s SoC product lines MOUNTAIN VIEW, Calif ., – August 10, 2016 – CEVA, Inc. (NASDAQ: CEVA), the leading licensor of signal processing IP for smarter, connected devices, today announced that Rockchip, China’s leading fabless semiconductor company, has licensed

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“NVIDIA VisionWorks, a Toolkit for Computer Vision,” a Presentation from NVIDIA

Algorithms & Models, NVIDIA, Processors, Software, Tools, Videos /

Elif Albuz, Technical Lead for the VisionWorks Toolkit at NVIDIA, presents the "NVIDIA VisionWorks, a Toolkit for Computer Vision" tutorial at the May 2016 Embedded Vision Summit. In this talk, Albuz introduces the NVIDIA VisionWorks toolkit, a software development package for computer vision and image processing. VisionWorks implements and extends the Khronos OpenVX standard, and

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“High-resolution 3D Reconstruction on a Mobile Processor,” a Presentation from Qualcomm

Algorithms & Models, Processors, Qualcomm, Sensors and Cameras, Software, Tools, Videos /

Michael Mangan, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit. Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors

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pyramid-image

Embedded Vision Application: A Design Approach for Real Time Classifiers

Articles, PathPartner Technology, Technical Articles /

This article was originally published at PathPartner Technology's website. It is reprinted here with the permission of PathPartner Technology. Object detection/classification is a supervised learning process in machine vision to recognize patterns or objects from images or other data. It is a major component in Advanced Driver Assistance Systems (ADAS), for example, as it is

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“Getting from Idea to Product with 3D Vision,” a Presentation from Intel and MathWorks

Algorithms & Models, Intel, MathWorks, Processors, Sensors and Cameras, Software, Tools, Videos /

Anavai Ramesh, Senior Software Engineer at Intel, and Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, present the "Getting from Idea to Product with 3D Vision" tutorial at the May 2016 Embedded Vision Summit. To safely navigate autonomously, cars, drones and robots need to understand their surroundings in three dimensions. While 3D vision

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FPGAs for Deep Learning-based Vision Processing

Articles, Intel, Technical Articles /

FPGAs have proven to be a compelling solution for solving deep learning problems, particularly when applied to image recognition. The advantage of using FPGAs for deep learning is primarily derived from several factors: their massively parallel architectures, efficient DSP resources, and large amounts of on-chip memory and bandwidth. An illustration of a typical FPGA architecture

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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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