论文标题
EKYA：在边缘计算服务器上持续学习视频分析模型
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
论文作者
论文摘要
视频分析应用程序使用Edge Compute服务器进行视频分析（用于带宽和隐私）。用于推理的边缘服务器上的压缩模型遭受数据漂移的影响，在该数据漂移中，实时视频数据与培训数据有所不同。连续学习通过定期在新数据上重新检验模型来处理数据漂移。我们的工作解决了边缘服务器上共同支持推理和重新培训任务的挑战，这需要在重新训练模型的准确性和推理准确性之间导航基本权衡。我们的解决方案EKYA平衡了这种跨多种模型的权衡，并使用微型企业来识别将通过再培训受益最大的模型。与基线调度程序相比，Ekya的准确性增长率高29％，基线需要更多4倍的GPU资源才能达到与Ekya相同的准确性。
Video analytics applications use edge compute servers for the analytics of the videos (for bandwidth and privacy). Compressed models that are deployed on the edge servers for inference suffer from data drift, where the live video data diverges from the training data. Continuous learning handles data drift by periodically retraining the models on new data. Our work addresses the challenge of jointly supporting inference and retraining tasks on edge servers, which requires navigating the fundamental tradeoff between the retrained model's accuracy and the inference accuracy. Our solution Ekya balances this tradeoff across multiple models and uses a micro-profiler to identify the models that will benefit the most by retraining. Ekya's accuracy gain compared to a baseline scheduler is 29% higher, and the baseline requires 4x more GPU resources to achieve the same accuracy as Ekya.
