Neuromemristive Circuits for Edge Computing: A Review

被引：200

作者：

Krestinskaya, Olga ^{[1
]}

James, Alex Pappachen ^{[1
]}

Chua, Leon Ong ^{[2
]}

机构：

[1] Nazarbayev Univ, Elect & Comp Engn Dept, Astana 010000, Kazakhstan

[2] Univ Calif Berkeley, Elect Engn & Comp Sci Dept, Berkeley, CA 94720 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 01期

关键词：

Computer architecture; Edge computing; Neuromorphics; Hardware; Memristors; Cloud computing; Data processing; Cellular neural network (CeNN); convolutional neural network (CNN); deep learning neural network; hierarchical temporal memory (HTM); long short-term memory (LSTM); memristor circuits; memristors; neural networks; spiking neural networks (SNNs); CONVOLUTIONAL NEURAL-NETWORKS; NEUROMORPHIC NETWORK; SPATIAL POOLER; MEMRISTOR; SYSTEM; ARCHITECTURE; DESIGN; STORAGE; CLASSIFICATION; PERFORMANCE;

D O I：

10.1109/TNNLS.2019.2899262

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：4 / 23

页数：20

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