The interesting part of edge AI is not that a model runs locally. It is that the product can make decisions without waiting for the network. This is an English DEV.to draft based on a Silicon LogiX technical article. The canonical source is linked at the end. Why it matters NPUs are appearing inside embedded SoCs because CPU-only inference is often too slow or too power hungry. Local inference can reduce latency, bandwidth, privacy exposure and cloud operating costs. Architecture notes A useful edge AI pipeline includes acquisition, preprocessing, inference, postprocessing and confidence handling. The NPU rarely replaces the CPU. It accelerates a narrow part of the pipeline. Model format, quantization and operator support matter as much as advertised TOPS. The application needs fallbacks for low confidence, drift and sensor degradation. Practical checklist [ ] Benchmark the exact model on the exact accelerator. [ ] Measure end-to-end latency, not only inference time.…