动态划分神经网络层，实现边缘云环境的节能降时。
Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum
- 根据运行时状态实时分割模型层，适应硬件与网络变化。
- 相比静态划分，能耗降低27.09%~35.82%，延迟减少6.34%~22.92%。
- 适用于资源受限设备上的AI推理，实测验证效果显著。
近年来，人工智能在资源受限的物联网设备上应用日益广泛。然而，现有边缘-云连续体中的AI任务划分与卸载方法多采用静态策略，忽略运行时动态变化，且常在模拟环境中评估而非真实硬件。为此，我们提出一种动态框架，将神经网络层跨异构连续体进行智能分割。该框架在启动时对模型进行剖析，测量节点间网络状况，并周期性重新评估划分以适应环境变化。我们构建了由树莓派边缘设备、笔记本雾节点和高性能桌面云组成的物理测试平台，评估了VGG16、AlexNet和MobileNetV2三个主流卷积神经网络。结果表明，与静态划分基线相比，该框架在能耗上降低27.09%至35.82%，端到端延迟减少6.34%至22.92%，证实了自适应划分优于静态方案。
原文摘要 · Abstract (English)
In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to AI task partitioning and offloading across the edge-cloud continuum typically rely on static methods that ignore runtime dynamics. Furthermore, they are often evaluated in simulated environments rather than on real hardware. To address this gap, we propose a framework that dynamically splits neural network layers across the heterogeneous continuum. The framework profiles the model at startup, measures network link conditions between nodes, and periodically re-evaluates the partition to adapt to environmental changes. We created a physical testbed comprising a Raspberry Pi edge device, a laptop fog, and a high-performance desktop PC as the cloud. We evaluated the framework over three widely adopted convolutional neural networks: VGG16, AlexNet, and MobileNetV2. Our results show that the framework achieves reductions in energy and end-to-end latency of 27.09--35.82% and 6.34--22.92%, respectively, compared to a static partitioning baseline. These findings confirm the superiority of adaptive to static partitioning.
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