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用于边缘计算的神经忆阻器电路:综述

Neuromemristive Circuits for Edge Computing: A Review.

作者信息

Krestinskaya Olga, James Alex Pappachen, Chua Leon Ong

出版信息

IEEE Trans Neural Netw Learn Syst. 2020 Jan;31(1):4-23. doi: 10.1109/TNNLS.2019.2899262. Epub 2019 Mar 14.

Abstract

The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.

摘要

连接到互联网的传感器网络不断增加,由此产生的数据的容量、准确性、可变性和速度,给云计算基础设施的电源管理、可扩展性和可持续性带来了挑战。以更低的功耗要求提高边缘计算设备的数据处理能力,可以减少云计算解决方案的多项开销。本文综述了可集成到边缘计算设备中的神经形态互补金属氧化物半导体忆阻架构。我们讨论了神经形态架构为何对边缘设备有用,并展示了用于边缘计算的神经忆阻电路领域的优势、缺点和未解决的问题。

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