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 […]
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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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
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
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
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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Yury Gorbachev, Principal Engineer at Itseez (now part of Intel), presents the "Real-world Vision Systems Design: Challenges and Techniques" tutorial at the May 2016 Embedded Vision Summit. Computer vision is central to many modern, cool products and technologies, including augmented reality, virtual reality and drones. Thanks to recent advances in system-on-chip and embedded systems design,
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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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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
Pierre Paulin, Senior R&D Director for Embedded Vision at Synopsys, presents the "Programming Embedded Vision Processors Using OpenVX" tutorial at the May 2016 Embedded Vision Summit. OpenVX, a new Khronos standard for embedded computer vision processing, defines a higher level of abstraction for algorithm specification, with the goal of enabling platform and tool innovation in
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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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