With the development of deep neural networks, the smart edge-cloud scenario is expected to meet the diverse and personalized requirements of users. However, the exiting edge-cloud architecture requires the deployment of manually designed neural networks. Providing adaptive network architecture in this environment, especially for heterogeneous edge devices, becomes the major challenge. To this end, we design the customized network architecture with neural architecture search for adaptively neural network search. Specifically, our architecture can be identified in three parts: cloud layer, equipped with huge amounts of computing resources, conducts multi-objective architecture search on a proxy dataset; the edge layer aims to train the optimal architecture based on the target dataset from scratch, which makes full use of the computing resource of the edge server; and the user layer deploys the multi-objective model in diverse devices for inference in real time. Furthermore, we deploy an industrial sensor monitoring scenario as a case study to search for the temporal convolutional network, demonstrating the effectiveness of the proposed architecture. Experimental results show the feasibility of the proposed architecture.

中文翻译：

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一种用于协同边缘云计算的自适应神经架构搜索设计

随着深度神经网络的发展，智能边缘云场景有望满足用户多样化和个性化的需求。然而，现有的边缘云架构需要部署手动设计的神经网络。在这种环境中提供自适应网络架构，特别是对于异构边缘设备，成为主要挑战。为此，我们设计了具有自适应神经网络搜索的神经架构搜索的定制网络架构。具体来说，我们的架构可以分为三个部分：云层，配备海量计算资源，对代理数据集进行多目标架构搜索；边缘层旨在从头开始基于目标数据集训练最佳架构，充分利用边缘服务器的计算资源；用户层在不同的设备中部署多目标模型进行实时推理。此外，我们部署了一个工业传感器监控场景作为案例研究来搜索时间卷积网络，证明了所提出架构的有效性。实验结果表明了所提出架构的可行性。