Chapter 09 · Sustainability & AISustainability & AI
Edge AI
Definition
The deployment of AI models directly on local devices — sensors, cameras, phones, microcontrollers — rather than in centralised data centres.
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Running inference at the edge reduces the need to transmit raw data, cuts latency and allows systems to work where connectivity is poor or power is scarce. It relies on compact, efficient models; quantisation and distillation are typical enablers.
How it is used
Edge AI suits distributed environmental monitoring: acoustic sensors identifying species in forests, camera traps filtering images on-device, smart meters analysing consumption locally, and buoys or field stations operating off-grid.
Why it matters
Conservation and agriculture often happen far from reliable networks and power. Edge AI extends automated observation into exactly those places — while its efficiency constraints align with the frugal-AI agenda of doing more with less compute.