Neuromemristive Circuits for Edge Computing: A Review

Olga Krestinskaya, Alex Pappachen James, Leon Ong Chua

Research output: Contribution to journalReview article

5 Citations (Scopus)

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.

Original languageEnglish
Article number8667457
Pages (from-to)4-23
Number of pages20
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number1
DOIs
Publication statusPublished - Jan 2020

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Keywords

  • 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)

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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