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Artificial neural networks based on memristive devices

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  • Special Focus on Memristive Devices and Neuromorphic Computing
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Abstract

The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true “thinking machine”. Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs, relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor (CMOS) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOS networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA

    Vignesh Ravichandran, Can Li, Ali Banagozar, J. Joshua Yang & Qiangfei Xia

Authors
  1. Vignesh Ravichandran
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  2. Can Li
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  3. Ali Banagozar
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  4. J. Joshua Yang
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  5. Qiangfei Xia
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Correspondence to Qiangfei Xia.

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Ravichandran, V., Li, C., Banagozar, A. et al. Artificial neural networks based on memristive devices. Sci. China Inf. Sci. 61, 060423 (2018). https://doi.org/10.1007/s11432-018-9425-1

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  • DOI: https://doi.org/10.1007/s11432-018-9425-1

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