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Processors/DSPs  

Imaging explosion requires mobile architecture rethink

Posted: 11 Mar 2015     Print Version  Bookmark and Share

Keywords:Imaging  embedded devices  smartphone  augmented reality  CPU 

Firstly, while the algorithms at the root of these capabilities are well known and established, they have not been adopted for mobile or embedded systems. Sure, we've seen embedded implementations for Haar and ORB in mobile computing platforms, but what about convolutional neural networks (CNN) for deep learning? These have been good on desktops and for cloud processing of acquired images. It is no mean feat to bring human vision and intelligent processing algorithms and apply them in the mobile domain with its limited power and space. Companies have shown demonstrations, but none have emerged as products, yet.

Secondly, these additional features and heavy algorithms are being added to handsets that have been using the same basic architectures—CPU, GPU, DSP—that in many cases have only been through minor technological upgrades. The lifespan of this approach may be limited.

Another approach is to acknowledge the need for CPU and GPU, but then redefine the DSP role. Gone are the days where full-fledged DSPs with their flexibility and programmability were useful for general-purpose signal processing tasks. Where most opportunities lie for innovation, is in the development of specific processor IP to handle 3D imaging and intelligent vision functions. To be effective, these processors need to be extremely low power and highly optimised for the task at hand. If applied on a broader scale, it could almost be considered to be almost a 'swarm' approach to signal processing, whereby processor IP is pervasive on a device, but harnessed and intelligently applied to optimise for power, space, programmability and cost for specific applications (imaging and vision being a good example).

Expanding up upon this idea, in light of the emerging Internet of Things, the macro could be embedded devices themselves acting as swarm processing nodes that are collecting, processing, locally analysing and then uploading interesting data to be collected and further analysed as part of a bigger, connected-vision paradigm.

Where this takes us is open to our imagination. In the meantime, let's take some time to rethink the hardware/software processing divide and functional allocation. Thoughts welcome.

About the author
Eran Briman is vice president of marketing for DSP-core company CEVA.


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