6 reasons deep learning accelerators need vision processors

By videantis GmbH

December 3, 2018

Since AlexNet in 2012, deep learning (DL) has taken the world of image processing by storm. Working on vision applications for automotive, smartphones, data centers, augmented reality, or using image processing in any shape or form? Then you’re probably either already using deep learning techniques or looking to adopt them. Since deep learning consumes very high compute resources, typically several TOPS, many SOC architects are adding specific deep learning accelerators to their designs to provide the required computational power. But when you’re looking to add smart camera sensing capabilities to your device, just adding a deep learning accelerator isn’t really enough. You also need a vision processor that efficiently runs image processing and classical computer vision (CV) algorithms. Let’s look at some reasons why.

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