AI at the Edge

Edge AI brings trained models out of the data center and into the field—running directly on single-board computers, smart sensors, kiosks, and industrial HMIs. This tag documents practical techniques for deploying neural networks under strict power and thermal budgets, where every watt and millisecond matter. You’ll find guides on model selection (CNNs, transformers, tinyML), quantization and pruning to fit RAM/flash limits, and acceleration options available on modern SBCs: CPUs with vector extensions, integrated GPUs, NPUs, and heterogeneous pipelines. We also cover dataset curation for edge scenarios, streaming inference, offline fallback, and privacy-by-design architectures that keep sensitive data local. Whether you are building quality inspection on a factory line, people counting in retail, or anomaly detection in utilities, the articles consolidate design patterns, real measurements, and troubleshooting tips so teams can move from proof-of-concept to reliable 24/7 deployment.