  Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide
range of fields, ranging from speech processing, image classification to drug
discovery. This is driven by the explosive growth of data, advances in machine
learning (especially deep learning), and easy access to vastly powerful
computing resources. Particularly, the wide scale deployment of edge devices
(e.g., IoT devices) generates an unprecedented scale of data, which provides
the opportunity to derive accurate models and develop various intelligent
applications at the network edge. However, such enormous data cannot all be
sent from end devices to the cloud for processing, due to the varying channel
quality, traffic congestion and/or privacy concerns. By pushing inference and
training processes of AI models to edge nodes, edge AI has emerged as a
promising alternative. AI at the edge requires close cooperation among edge
devices, such as smart phones and smart vehicles, and edge servers at the
wireless access points and base stations, which however result in heavy
communication overheads. In this paper, we present a comprehensive survey of
the recent developments in various techniques for overcoming these
communication challenges. Specifically, we first identify key communication
challenges in edge AI systems. We then introduce communication-efficient
techniques, from both algorithmic and system perspectives for training and
inference tasks at the network edge. Potential future research directions are
also highlighted.
