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“Deep-learning-based Visual Perception in Mobile and Embedded Devices: Opportunities and Challenges,” a Presentation from Qualcomm

Jeff Gehlhaar, Vice President of Technology, Corporate Research and Development, at Qualcomm, presents the "Deep-learning-based Visual Perception in Mobile and Embedded Devices: Opportunities and Challenges" tutorial at the May 2015 Embedded Vision Summit. Deep learning approaches have proven extremely effective for a range of perceptual tasks, including visual perception. Incorporating deep-learning-based visual perception into devices […]

“Deep-learning-based Visual Perception in Mobile and Embedded Devices: Opportunities and Challenges,” a Presentation from Qualcomm Read More +

“Understanding Adaptive Machine Learning Vision Algorithms and Implementing Them on GPUs and Heterogeneous Platforms,” a Presentation from AMD

Harris Gasparakis, OpenCV Manager at AMD, presents the "Understanding Adaptive Machine Learning Vision Algorithms and Implementing them on GPUs and Heterogeneous Platforms" tutorial at the May 2015 Embedded Vision Summit. Machine learning algorithms are pervasive in computer vision: from object detection to object tracking to full scene recognition, generative or discriminative learning dominates the space,

“Understanding Adaptive Machine Learning Vision Algorithms and Implementing Them on GPUs and Heterogeneous Platforms,” a Presentation from AMD Read More +

“Creating Smarter, More Interactive Apps and Systems with Computer Vision,” a Presentation from the Embedded Vision Alliance

Thanks to improvements in processors, image sensors, and algorithms, more and more of our devices now — for the first time — are gaining the ability to see and understand the world around them. In this talk from the June 2015 Augmented World Expo, Jeff Bier (Founder of the Embedded Vision Alliance) highlights the opportunities

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“Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs,” a Presentation From Altera

Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit. Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized

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“Developing High-Performance, Energy-Efficient Vision Solutions Using Open-Standard Libraries and APIs,” a Presentation from Intel

Mostafa Hagog, Architect at Intel, presents the "Developing High-Performance, Energy-Efficient Vision Solutions Using Open-Standard Libraries and APIs" tutorial at the May 2015 Embedded Vision Summit. Connected cameras, hand-held devices, embedded systems, and cloud services enable a world of intelligence everywhere. But to realize the potential of computer vision technology, developers face tough challenges. On the

“Developing High-Performance, Energy-Efficient Vision Solutions Using Open-Standard Libraries and APIs,” a Presentation from Intel Read More +

Sorting Out Embedded Vision Systems

This article was originally published on June 10, 2015 at Altera's website. It is reprinted here with the permission of Altera. Papers at this year’s Embedded Vision Summit suggested the vast range of ways that embedded systems can employ focused light as an input, and the even vaster range of algorithms and hardware implementations they

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“The Evolution of Object Recognition in Embedded Systems,” a Presentation from CEVA

Moshe Shahar, Director of System Architecture at CEVA, presents the "Evolution of Object Recognition in Embedded Systems" tutorial at the May 2015 Embedded Vision Summit. Camera-enabled devices have made great strides in performance and quality in recent years, but they still fall far short of human visual perception. To reach their potential, vision-enabled systems must

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“Designing and Selecting Instruction Sets for Vision,” a Presentation From Cadence

Chris Rowen, Fellow at Cadence, presents the "Designing and Selecting Instruction Sets for Vision" tutorial at the May 2015 Embedded Vision Summit. Two critical technical trends have reached important inflection points: the massive compute demands of vision processing and the capabilities of specialized vision processors. But what how do you actually select (or even build)

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“Choosing a Processor for Embedded Vision: Options and Trends,” a Presentation From BDTI

Jeff Bier, President of Berkeley Design Technology, Inc. (BDTI) and Founder of the Embedded Vision Alliance, presents the "Choosing a Processor for Embedded Vision: Options and Trends" tutorial at the May 2015 Embedded Vision Summit. Computer vision applications typically demand lots of processor performance. These applications also tend to be complex and fast-changing, so developers

“Choosing a Processor for Embedded Vision: Options and Trends,” a Presentation From BDTI Read More +

May 2015 Embedded Vision Summit Technical Presentation: “System-level Design for Embedded Vision with FPGA-based Programmable SoCs,” Mario Bergeron, Avnet Electronics Marketing

Mario Bergeron, Technical Marketing Engineer at Avnet Electronics Marketing, presents the "System-Level Design for Embedded Vision with FPGA-based Programmable SoCs" tutorial at the May 2015 Embedded Vision Summit. FPGA-based programmable system-on-chip (SoC) devices offer capabilities beyond those found in traditional embedded processors. The programmability and vast parallel processing capabilities of the FPGA fabric allow developers

May 2015 Embedded Vision Summit Technical Presentation: “System-level Design for Embedded Vision with FPGA-based Programmable SoCs,” Mario Bergeron, Avnet Electronics Marketing 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.

Contact

Address

Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596

Phone
Phone: +1 (925) 954-1411
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