Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable to generate tailored model architectures for different conditions. However, conventional pre-deployment model generation approaches are not satisfactory due to the difficulty of handling the diversity of edge environments and the demand for edge information. In this paper, we propose to adapt the model architecture after deployment in the target environment, where the model quality can be precisely measured and private edge data can be retained. To achieve efficient and effective edge model generation, we introduce a pretraining-assisted on-cloud model elastification method and an edge-friendly on-device architecture search method. Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model. Each subnet in the space is a valid model with different environment affinity, and each device efficiently finds and maintains the most suitable subnet based on a series of edge-tailored optimizations. Extensive experiments on various edge devices demonstrate that our approach is able to achieve significantly better accuracy-latency tradeoffs (e.g. 46.74\% higher on average accuracy with a 60\% latency budget) than strong baselines with minimal overhead (13 GPU hours in the cloud and 2 minutes on the edge server).

翻译：深度学习模型越来越多地被部署到用于实时应用的边缘设备中。为确保不同边缘环境的稳定服务质量,非常可取的做法是为不同条件建立定制的模型结构;然而,传统的部署前模型生成方法并不令人满意,因为难以处理边缘环境的多样性和对边缘信息的需求。在本文件中,我们提议在目标环境部署后调整模型结构,以便精确测量模型质量,并保留私人边缘数据。为了实现高效率和高效益的边缘模型生成,我们引入了一种预先培训辅助的悬浮模型弹性模型弹性模型和一种对边缘方便的离子结构搜索方法。模型弹性模型生成方法产生了一个高质量的模型结构搜索空间,其使用开发者指定或触角模型的指南。在空间的每个子网都是一个有效的模型,其环境相似性不同,每个设备都能够高效率地发现和维护基于一系列边缘相近优化的最适合的子网络。在各种边缘设备上进行广泛的实验表明,我们的方法能够大大改进精确度的延迟度交易(例如46.74英寸+2英寸平均空端服务器的精确度比60分钟最低的高级预算基线(13+G+10)。</s>