Also known as: on-device AI, edge inference
Edge AI is the practice of running machine-learning models directly on local devices — single-board computers, cameras, sensors — instead of sending data to the cloud for inference.1
Overview
The appeal is concrete: inference next to the data is lower-latency, keeps private data on the device, and keeps working without a network. The cost is that small devices have limited compute, so edge AI leans on efficient, quantised models and on dedicated AI accelerators such as Google Coral’s Edge TPU or the GPU on an NVIDIA Jetson. It is a specific case of the broader move toward edge computing.
Where it fits
Edge AI shows up wherever round-tripping to a server is too slow, too costly, or impossible: factory cameras, doorbells, robots, and home automation. In a GopherTrunk-style deployment, edge AI could classify or flag activity in decoded data on the same node that does the radio work, sending up only the results rather than every sample.
Sources
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Edge computing — Wikipedia, on processing data near its source, including on-device inference. ↩