The Status of AI at the Edge? It's Complicated

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True AI at the edge, meaning running neural networks on the smart device itself, is a thorny set of problems.

www.eetasia.com, Dec. 14, 2021 – 

"Edge AI" is something of a misnomer. Most smart devices, IoT, and other edge implementations don't actually process data at the edge. Edge devices aren't like smartphones or tablets, equipped with a processor and storage and software, able to perform compute tasks.

What most would call edge AI is cloud-based AI processing of data collected at the edge. The results are then sent back to the device and often back to the cloud for further processing, aggregation, and centralization.

Edge devices are largely "dumb" in that sense. Your smart home-automation gadget isn't terribly smart. It records your command, from your voice, from its companion app, or from a setting you've configured previously. Perhaps it does some minor preprocessing. It then sends the command over its internet connection to a physical server in a data center somewhere, and it waits for its instruction to turn the light on or off.

This edge-to-cloud workflow, obviously, takes time. Fortunately, edge-to-cloud works well for many applications.

Unfortunately, AI is not one of them.

AI at the edge – true AI at the edge, meaning running neural networks on the smart device itself – is a thorny problem, or set of problems: limited processing resources, small storage capacities, insufficient memory, security concerns, electrical power requirements, limited physical space on devices. Another major obstacle to designing edge devices capable of AI processing at the edge is excessive cost. Few consumers could afford to upgrade to smart light bulbs if each one cost the equivalent of an iPhone.

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