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Synopsys

By effectively using all compute resources, along with leveraging software toolsets, it's possible to deliver robust AR in embedded designs.

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Fergus Casey of Synopsys delivers an Enabling Technologies presentation at the May 2017 Embedded Vision Summit.

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Tom Michiels of Synopsys delivers a Technical Insights presentation at the May 2018 Embedded Vision Summit.

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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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Pierre Paulin of Synopsys delivers an Enabling Technologies presentation at the May 2017 Embedded Vision Summit.

Academy

Tom Michiels of Synopsys delivers a Technical Insights presentation at the May 2017 Embedded Vision Summit.

Academy

Real-time assessments of age range, gender, ethnicity, gaze direction, attention span, emotional state and other attributes are now possible

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Seema Mirchandaney of Synopsys delivers an enabling technologies presentation at the May 2016 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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