边缘人工智能推理

Edge AI inference is the execution of trained machine‑learning models locally on edge devices (sensors, gateways, mobile and embedded systems) to produce predictions or decisions in real time at the data source. It reduces latency, bandwidth use, and data exposure compared with cloud inference, but requires hardware‑aware model optimization (quantization, pruning, distillation), efficient scheduling and often dedicated accelerators (NPUs/GPUs/DSPs) to satisfy tight power, memory and thermal constraints. In product design and production this mandates cross‑functional tradeoffs among accuracy, cost, security and updateability, plus robust deployment pipelines, on‑device monitoring and OTA model management to ensure reproducible performance and regulatory compliance over the product lifecycle.