论文标题
深边缘智能中最先进的技术
State-of-the-art Techniques in Deep Edge Intelligence
论文作者
论文摘要
机器学习技术和最近深度学习确实解锁了整个网络中生成的数据的潜力。后者提供的优点已经看到它迅速成为各种应用程序的首选框架。但是，计算资源的集中化以及对数据聚合的需求长期以来一直是深度学习应用民主化的限制因素。边缘计算是一种新兴范式，旨在利用网络外围可用的迄今未开发的处理资源。 Edge Intelligence（EI）迅速成为使用边缘计算概念启用学习的有力替代方法。基于深度学习的边缘智能或深度边缘智能（DEI）在于这个快速发展的领域。在本文中，我们概述了操作DEI的主要限制。 DEI的主要研究途径已在联邦学习，分布式计算，压缩方案和条件计算下合并。我们还提出了一些普遍的挑战，并突出了前瞻性研究途径。
The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learning. The advantages offered by the latter have seen it rapidly becoming a framework of choice for various applications. However, the centralization of computational resources and the need for data aggregation have long been limiting factors in the democratization of Deep Learning applications. Edge Computing is an emerging paradigm that aims to utilize the hitherto untapped processing resources available at the network periphery. Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. Deep Learning-based Edge Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving domain. In this article, we provide an overview of the major constraints in operationalizing DEI. The major research avenues in DEI have been consolidated under Federated Learning, Distributed Computation, Compression Schemes and Conditional Computation. We also present some of the prevalent challenges and highlight prospective research avenues.
