NEURO-MEMRISTIVE CIRCUITS FOR EDGE COMPUTING: A REVIEW

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1807.00962.pdf (4.2 MB)

Date

2018

Authors

Krestinskaya, Olga
James, Alex
Chua, Leon O.

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IEEE Transactions on Neural Networks and Learning Systems

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 neuro morphic 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 neuro-memristive circuits for edge computing.

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Keywords

Type of access: Open Access, Memristors, Memristor circuits, Neural Networks, Cellular neural network, Convolutional neural network, Long short-term memory, Hierarchical temporal memory, Spiking neural networks, Deep neural networks

Citation

Krestinskaya, O., James, A. P., & Chua, L. O. (2020). Neuromemristive Circuits for Edge Computing: A Review. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 4–23. https://doi.org/10.1109/tnnls.2019.2899262

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http://nur.nu.edu.kz/handle/123456789/6450

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Attribution-NonCommercial-ShareAlike 3.0 United States
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