embedded vision

September 23, 2018
Gordon Cooper

Flexible embedded vision processing architectures for machine-learning applications

Machine-learning strategies for embedded vision are evolving so quickly that designers need access to flexible, heterogenous processor architectures that can adapt as the algorithms evolve.
Expert Insight  |  Topics: Embedded - Architecture & Design, IP - Selection  |  Tags: , ,   |  Organizations:
February 27, 2018
Gordon Cooper

Optimizing power and performance trade-offs in CNN implementations for embedded vision

High-performance vision-processing algorithms need optimized CNN engines to deliver the right performance within the power budget of embedded applications.
June 29, 2017
Gordon Cooper

High-resolution visual recognition needs high-performance CNNs

Quadrupling the performance of a dedicated CNN engine within an embedded vision processing core brings more complex graph processing within reach.
Expert Insight  |  Topics: Embedded - Architecture & Design, IP - Selection  |  Tags: , , , , ,   |  Organizations:
April 4, 2017

Teaching computers to recognize a smile (or frown, or grimace or…)

Using deep learning techniques and convolutional neural networks to bring facial recognition capabilities to embedded systems.
April 20, 2015
Michael Thompson is the senior manager of product marketing for the DesignWare ARC processors at Synopsys

Neural networks bring advanced object detection to embedded vision

Dedicated processors using convolutional neural networking techniques bring advanced vision techniques such as object recognition to embedded systems.

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