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Dec, 2020;32 (51): e2004659.

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing.

Sanghyeon Choi, Jehyeon Yang, Gunuk Wang
Author Information
  1. Sanghyeon Choi: KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
  2. Jehyeon Yang: KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
  3. Gunuk Wang: KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea. ORCID
PMID: 33006204 DOI: 10.1002/adma.202004659

Abstract

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

Keywords

artificial neural networks artificial neurons artificial synapses memristive electronic devices memristors neuromorphic electronics

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Grants

  1. NRF-2020M3F3A2A03082825/National Research Foundation of Korea
  2. NRF-2019R1A2C2003704/National Research Foundation of Korea
  3. /KU-KIST Research Fund
  4. /Samsung Electronics
  5. /Korea University

MeSH Term

Neural Networks, Computer
Neurons
Synapses
Algorithms
Electronics
Journal Article Review

Word Cloud

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