异构边缘-云协同的 AI 任务动态切分框架 Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum

精读摘要

资源受限 IoT 设备上的 AI 使用增长显著，但现有边缘-云任务切分与卸载方案多为静态方法，忽视运行时动态，且常在仿真环境而非真实硬件上评估。这项研究提出一个在异构边缘-云连续体上动态切分神经网络层的框架：启动时对模型画像，运行中按实际状态做安全卸载。真实硬件验证让它更接近工业落地需求。 AI on resource-constrained IoT devices has grown significantly, but existing task partitioning and offloading across the edge-cloud continuum rely on static methods that ignore runtime dynamics and are often evaluated in simulation. This framework dynamically splits neural network layers across the heterogeneous continuum, profiling the model at startup and offloading safely at runtime, validated on real hardware.