Resource-constrained IoT devices, such as sensors and actuators, have become
ubiquitous in recent years. This has led to the generation of large quantities
of data in real-time, which is an appealing target for AI systems. However,
deploying machine learning models on such end-devices is nearly impossible. A
typical solution involves offloading data to external computing systems (such
as cloud servers) for further processing but this worsens latency, leads to
increased communication costs, and adds to privacy concerns. To address this
issue, efforts have been made to place additional computing devices at the edge
of the network, i.e close to the IoT devices where the data is generated.
Deploying machine learning systems on such edge computing devices alleviates
the above issues by allowing computations to be performed close to the data
sources. This survey describes major research efforts where machine learning
systems have been deployed at the edge of computer networks, focusing on the
operational aspects including compression techniques, tools, frameworks, and
hardware used in successful applications of intelligent edge systems.

本文综述了在边缘计算设备上部署机器学习系统的主要研究工作，重点关注了智能边缘系统成功应用中使用的压缩技术、工具、框架和硬件等方面的运营问题。