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Resources on Embedded Vision from BDTI

Access BDTI resources via the links below. Use the links to the right (Videos, Documents, and Downloads) for listings by type.

For machine learning to deliver its potential, it requires sufficient high quality training data, plus developer knowledge of how to use it.

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This article reviews deep learning implementation options such as heterogeneous processing, network quantization, and software optimizations

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Deep learning-based vision processing is an increasingly popular and robust alternative to classical computer vision algorithms.

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Shehrzad Qureshi of BDTI delivers a Fundamentals presentation at the May 2017 Embedded Vision Summit.

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Deep learning has been enabled by, among other things, the steadily increasing processing "muscle" of CPUs aided by co-processors.

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OpenVX enables embedded vision application software developers to efficiently harness the processing resources available in SoCs and systems

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Jeff Bier of BDTI and the Embedded Vision Alliance delivers a technical presentation at the May 2015 Embedded Vision Summit.

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OpenCL enables software developers to efficiently harness diverse processing resources in embedded vision.

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Jeff Bier of BDTI delivers a technical presentation at the May 2014 Embedded Vision Summit.

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Jeff Bier of BDTI delivers a technical presentation at the May 2014 Embedded Vision Summit.

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