Processors

“How Computer Vision Is Accelerating the Future of Virtual Reality,” a Presentation from AMD

Allen Rush, Fellow at AMD, presents the "How Computer Vision Is Accelerating the Future of Virtual Reality" tutorial at the May 2016 Embedded Vision Summit. Virtual reality (VR) is the new focus for a wide variety of applications including entertainment, gaming, medical, science, and many others. The technology driving the VR user experience has advanced […]

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“Is Vision the New Wireless?,” a Presentation from Qualcomm

Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, presents the "Is Vision the New Wireless?" tutorial at the May 2016 Embedded Vision Summit. Over the past 20 years, digital wireless communications has become an essential technology for many industries, and a primary driver for the electronics industry. Today, computer vision is showing

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“Efficient Convolutional Neural Network Inference on Mobile GPUs,” a Presentation from Imagination Technologies

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Efficient Convolutional Neural Network Inference on Mobile GPUs" tutorial at the May 2016 Embedded Vision Summit. GPUs have become established as a key tool for training of deep learning algorithms. Deploying those algorithms on end devices is a key enabler to their commercial success and

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“Accelerating Deep Learning Using Altera FPGAs,” a Presentation from Intel

Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit. While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance

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“Fast Deployment of Low-power Deep Learning on CEVA Vision Processors,” a Presentation from CEVA

Yair Siegel, Director of Segment Marketing at CEVA, presents the "Fast Deployment of Low-power Deep Learning on CEVA Vision Processors" tutorial at the May 2016 Embedded Vision Summit. Image recognition capabilities enabled by deep learning are benefitting more and more applications, including automotive safety, surveillance and drones. This is driving a shift towards running neural

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“Lessons Learned from Bringing Mobile and Embedded Vision Products to Market,” a Presentation from ARM

Tim Hartley, Product Manager in the Personal Mobile Compute Business Line at ARM, presents the "Lessons Learned from Bringing Mobile and Embedded Vision Products to Market" tutorial at the May 2016 Embedded Vision Summit. Great news: technology is finally at a point where we can build sophisticated computer vision applications that run on mass market

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“Making Computer Vision Software Run Fast on Your Embedded Platform,” a Presentation from Luxoft

Alexey Rybakov, Senior Director at Luxoft, presents the "Making Computer Vision Software Run Fast on Your Embedded Platform" tutorial at the May 2016 Embedded Vision Summit. Many computer vision algorithms perform well on desktop class systems, but struggle on resource constrained embedded platforms. This how-to talk provides a comprehensive overview of various optimization methods that

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Optimizing Computer Vision Applications Using OpenCL and GPUs

The substantial parallel processing resources available in modern graphics processors makes them a natural choice for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across GPUs and other heterogeneous processing elements within a system. In this article, we briefly review parallelism in computer vision applications,

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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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“TensorFlow: Enabling Mobile and Embedded Machine Intelligence,” a Presentation from Google

Pete Warden, Research Engineer at Google, presents the "TensorFlow: Enabling Mobile and Embedded Machine Intelligence" tutorial at the May 2016 Embedded Vision Summit. Following a brief overview of the advances in deep learning and AI over the last few years, Pete discusses how Google uses TensorFlow to deploy those advances in products on mobile and

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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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