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Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations

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 […]

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“The Road Ahead for Neural Networks: Five Likely Surprises,” a Presentation from Cadence

Dr. Chris Rowen, Chief Technology Officer of the IP Group at Cadence, presents the "Road Ahead for Neural Networks: Five Likely Surprises" tutorial at the May 2016 Embedded Vision Summit. Cognitive computing is finally getting real! It has passed through the phases of obscurity and curiosity and is surviving the current phase of breathless hype.

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OpenVX Enables Portable, Efficient Vision Software

OpenVX, a maturing API from the Khronos Group, enables embedded vision application software developers to efficiently harness the various processing resources available in SoCs and systems. Vision technology is now enabling a wide range of products, that are more intelligent and responsive than before, and thus more valuable to users. Such image perception, understanding, and

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Using Convolutional Neural Networks for Image Recognition

This article was originally published at Cadence's website. It is reprinted here with the permission of Cadence. Convolutional neural networks (CNNs) are widely used in pattern- and image-recognition problems as they have a number of advantages compared to other techniques. This white paper covers the basics of CNNs including a description of the various layers

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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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May 2014 Embedded Vision Summit Technical Presentation: “Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking,” Chris Rowen, Cadence

Chris Rowen, Fellow at Cadence, presents the "Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking" tutorial at the May 2014 Embedded Vision Summit. This presentation looks at a cross-section of advanced feature detectors, and considers the algorithm, bit precision, arithmetic primitives and implementation optimizations that yield high pixel processing rates,

May 2014 Embedded Vision Summit Technical Presentation: “Taming the Beast: Performance and Energy Optimization Across Embedded Feature Detection and Tracking,” Chris Rowen, Cadence Read More +

April 2013 Embedded Vision Summit Technical Presentation: “Porting Applications to High-Performance Imaging DSPs,” Chris Rowen, Cadence

Chris Rowen, Fellow at Cadence and Founder of Tensilica, presents the "Porting Applications to High-Performance Imaging DSPs" tutorial within the "Developing Vision Software, Accelerators and Systems" technical session at the April 2013 Embedded Vision Summit. Rowen discusses the challenges of porting and tuning applications to a high-performance imaging DSP.  An imaging-specific DSP can make a

April 2013 Embedded Vision Summit Technical Presentation: “Porting Applications to High-Performance Imaging DSPs,” Chris Rowen, Cadence 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.

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