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Bringing AI To Edge: From Deep Learning's Perspective
arXiv - CS - Machine Learning Pub Date : 2020-11-25 , DOI: arxiv-2011.14808
Di Liu, Hao Kong, Xiangzhong Luo, Weichen Liu, Ravi Subramaniam

Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the development of edge intelligence systems encounters some challenges, and one of these challenges is the \textit{computational gap} between computation-intensive deep learning algorithms and less-capable edge systems. Due to the computational gap, many edge intelligence systems cannot meet the expected performance requirements. To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search. Although some reviews or surveys have partially covered this large body of literature, we lack a systematic and comprehensive review to discuss all aspects of these deep learning techniques which are critical for edge intelligence implementation. As various and diverse methods which are applicable to edge systems are proposed intensively, a holistic review would enable edge computing engineers and community to know the state-of-the-art deep learning techniques which are instrumental for edge intelligence and to facilitate the development of edge intelligence systems. This paper surveys the representative and latest deep learning techniques that are useful for edge intelligence systems, including hand-crafted models, model compression, hardware-aware neural architecture search and adaptive deep learning models. Finally, based on observations and simple experiments we conducted, we discuss some future directions.

中文翻译:

将AI带入边缘:深度学习的视角

边缘计算和人工智能(AI),尤其是当今的深度学习,正在逐渐相交以构建一个称为边缘智能的新型系统。但是,边缘智能系统的开发遇到了一些挑战,其中之一是计算密集型深度学习算法与能力较弱的边缘系统之间的\ textit {计算差距}。由于计算上的差距,许多边缘智能系统无法满足预期的性能要求。为了弥合差距,在过去几年中,提出了许多深度学习技术和优化方法:轻量级深度学习模型,网络压缩和有效的神经体系结构搜索。尽管一些评论或调查部分涵盖了这一庞大的文献,我们缺乏系统,全面的评论来讨论这些深度学习技术的各个方面,这些方面对于边缘智能实施至关重要。随着各种适用于边缘系统的方法被大量提出,整体审查将使边缘计算工程师和社区能够了解最先进的深度学习技术,这些技术对于边缘智能和开发都非常有用。边缘情报系统。本文调查了对边缘智能系统有用的代表性和最新的深度学习技术,包括手工制作的模型,模型压缩,硬件感知的神经体系结构搜索和自适应深度学习模型。最后,基于观察和我们进行的简单实验,我们讨论了一些未来的方向。
更新日期:2020-12-01
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