This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online knowledge distillation to improve the accuracy of models suffering from data drift and offloads the labeling process to the cloud, alleviating constrained resources of edge devices. At the edge, we design adaptive training using small batches to adapt models under limited computing power, and adaptive sampling of training frames for robustness and reducing bandwidth. The evaluations on the realistic dataset show 15%-20% model accuracy improvement compared to the edge-only strategy and fewer network costs than the cloud-only strategy.

翻译：本文提出Shoggoth，一种高效的边缘-云端协同架构，用于提升变场场景下实时视频的推理性能。Shoggoth采用在线知识蒸馏技术，改善因数据漂移导致准确率下降的模型，并将标注过程卸载至云端，以缓解边缘设备资源受限的问题。在边缘端，我们设计利用小批量数据进行自适应训练，使模型在有限计算能力下完成适配；同时通过自适应采样训练帧，兼顾鲁棒性与带宽优化。基于真实数据集的评估表明，相较于纯边缘策略，模型准确率提升15%-20%，且网络开销低于纯云端策略。