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Implementing Vision with Deep Learning in Resource-constrained Designs

DNNs (deep neural networks) have transformed the field of computer vision, delivering superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence, which had been solved quite well with conventional techniques, are now finding even better …

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Implementing High-performance Deep Learning Without Breaking Your Power Budget

This article was originally published at Synopsys' website. It is reprinted here with the permission of Synopsys. Examples of applications abound where high-performance, low-power embedded vision processors are used: a mobile phone using face recognition to identify a user, an augmented or mixed reality headset identifying your hands and the layout of your living room …

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The Evolution of Deep Learning for ADAS Applications

This technical article was originally published at Synopsys' website. It is reprinted here with the permission of Synopsys. Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it …

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Software Frameworks and Toolsets for Deep Learning-based Vision Processing

This article provides both background and implementation-detailed information on software frameworks and toolsets for deep learning-based vision processing, an increasingly popular and robust alternative to classical computer vision algorithms. It covers the leading available software framework options, the root reasons for their abundance, and guidelines for selecting an optimal approach among the candidates for a …

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“Designing Scalable Embedded Vision SoCs from Day 1,” a Presentation from Synopsys

Pierre Paulin, Director of R&D for Embedded Vision at Synopsys, presents the "Designing Scalable Embedded Vision SoCs from Day 1" tutorial at the May 2017 Embedded Vision Summit. Some of the most critical embedded vision design decisions are made early on and affect the design’s ultimate scalability. Will the processor architecture support the needed vision …

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“Moving CNNs from Academic Theory to Embedded Reality,” a Presentation from Synopsys

Tom Michiels, System Architect for Embedded Vision Processors at Synopsys, presents the "Moving CNNs from Academic Theory to Embedded Reality" tutorial at the May 2017 Embedded Vision Summit. In this presentation, you will learn to recognize and avoid the pitfalls of moving from an academic CNN/deep learning graph to a commercial embedded vision design. You …

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Facial Analysis Delivers Diverse Vision Processing Capabilities

Computers can learn a lot about a person from their face – even if they don’t uniquely identify that person. Assessments of age range, gender, ethnicity, gaze direction, attention span, emotional state and other attributes are all now possible at real-time speeds, via advanced algorithms running on cost-effective hardware. This article provides an overview of …

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“Using the OpenCL C Kernel Language for Embedded Vision Processors,” a Presentation from Synopsys

Seema Mirchandaney, Engineering Manager for Software Tools at Synopsys, presents the "Using the OpenCL C Kernel Language for Embedded Vision Processors" tutorial at the May 2016 Embedded Vision Summit. OpenCL C is a programming language that is used to write computation kernels. It is based on C99 and extended to support features such as multiple …

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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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“Programming Embedded Vision Processors Using OpenVX,” a Presentation from Synopsys

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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