Multi-State Memristors and Their Applications: An Overview

Author:

Wang Chaohan1ORCID,Si Zhaoguang1ORCID,Jiang Xiongfei2ORCID,Malik Adil1ORCID,Pan Yihan2ORCID,Stathopoulos Spyros2ORCID,Serb Alexander2ORCID,Wang Shiwei2ORCID,Prodromakis Themis2ORCID,Papavassiliou Christos1ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K

2. School of Engineering, The University of Edinburgh, Edinburgh, U.K

Funder

Engineering and Physical Sciences Research Council (EPSRC) Programme Grant Functional Oxides for Reconfigurable Technologies

Royal Academy of Engineering (RAEng) Chair in Emerging Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Reference89 articles.

1. Quantized neural networks: Training neural networks with low precision weights and activations;hubara;arXiv 1609 07061 [cs],2016

2. A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

3. Training and operation of an integrated neuromorphic network based on metal-oxide memristors;prezioso;Nature,2015

4. Face classification using electronic synapses

5. A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training;gonugondla;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2018

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