Architecting Low-Latency Inference Pipelines for Real-Time Edge Computing and Distributed Neural Networks
Introduction The convergence of edge computing and deep learning has opened the door to a new class of applications—real‑time perception, autonomous control, augmented reality, and industrial monitoring—all of which demand sub‑millisecond latency and high reliability. Unlike cloud‑centered AI services, edge inference must operate under strict constraints: limited compute, intermittent connectivity, power budgets, and often safety‑critical response times. Designing an inference pipeline that meets these requirements is not a simple matter of “run a model on a device.” It requires a holistic architecture that spans hardware acceleration, model engineering, data flow orchestration, and distributed coordination across many edge nodes. ...