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.
High-performance vision-processing algorithms need optimized CNN engines to deliver the right performance within the power budget of embedded applications.
Quadrupling the performance of a dedicated CNN engine within an embedded vision processing core brings more complex graph processing within reach.
Using deep learning techniques and convolutional neural networks to bring facial recognition capabilities to embedded systems.
Dedicated processors using convolutional neural networking techniques bring advanced vision techniques such as object recognition to embedded systems.
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