Embedded AI

Embedded AI brings perception and decision-making into devices with tight footprints. Compared with server workloads, the constraints are harsher: limited RAM, modest CPUs, small heatsinks, and hard real-time demands. Here we cover model compression, runtime selection, DMA-friendly data paths, and validation techniques that prevent edge cases from derailing field deployments. We emphasize reproducibility with versioned datasets and CI pipelines that test both accuracy and latency budgets. The goal is simple: AI features that survive beyond the prototype phase and remain serviceable for years.