This article was originally published as a two-part blog series at Cadence's website. It is reprinted here with the permission of Cadence. "Even if he gives the same presentation two weeks apart, it will be different.” —Neil Robinson, fellow attendee, on Chris Rowen My question: Is it because he is such a dynamic speaker, and […]
Visual Ventures with Chris Rowen Read More +
Samer Hijazi, Deep Learning Engineering Group Director at Cadence, presents the "Techniques to Reduce Power Consumption in Embedded DNN Implementations" tutorial at the May 2017 Embedded Vision Summit. Deep learning is becoming the most widely used technique for computer vision and pattern recognition. This rapid adoption is primarily driven by the outstanding effectiveness deep learning
Pulin Desai, Director of Product Marketing at Cadence, presents the "Scalable Neural Network Processors for Embedded Applications" tutorial at the May 2017 Embedded Vision Summit. This presentation describes the architecture of the latest Tensilica-based neural network processor IP and illustrates how easily different neural networks can be run using it. See how this low-power architecture
Frank Brill, Design Engineering Director at Cadence and Chairperson of the Khronos Group's OpenVX Working Group, presents the "OpenVX Computer Vision Library Standard for Portable, Efficient Code" tutorial at the May 2017 Embedded Vision Summit. OpenVX is an industry-standard computer vision API designed for efficient implementation on a variety of embedded platforms. The API incorporates
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 +
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.
“The Road Ahead for Neural Networks: Five Likely Surprises,” a Presentation from Cadence Read More +
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
OpenVX Enables Portable, Efficient Vision Software Read More +
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
Using Convolutional Neural Networks for Image Recognition Read More +
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)
“Designing and Selecting Instruction Sets for Vision,” a Presentation From Cadence Read More +
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,
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