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Hardware implementation of memristor-based artificial neural networks

Author

Listed:
  • Fernando Aguirre

    (King Abdullah University of Science and Technology (KAUST)
    Universitat Autònoma de Barcelona (UAB))

  • Abu Sebastian

    (IBM Research – Zurich)

  • Manuel Gallo

    (IBM Research – Zurich)

  • Wenhao Song

    (University of Southern California (USC))

  • Tong Wang

    (University of Southern California (USC))

  • J. Joshua Yang

    (University of Southern California (USC))

  • Wei Lu

    (University of Michigan)

  • Meng-Fan Chang

    (National Tsing Hua University)

  • Daniele Ielmini

    (Politecnico di Milano and IUNET)

  • Yuchao Yang

    (Peking University)

  • Adnan Mehonic

    (University College London (UCL), Torrington Place)

  • Anthony Kenyon

    (University College London (UCL), Torrington Place)

  • Marco A. Villena

    (King Abdullah University of Science and Technology (KAUST))

  • Juan B. Roldán

    (Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n)

  • Yuting Wu

    (University of Michigan)

  • Hung-Hsi Hsu

    (National Tsing Hua University)

  • Nagarajan Raghavan

    (Singapore University of Technology & Design)

  • Jordi Suñé

    (Universitat Autònoma de Barcelona (UAB))

  • Enrique Miranda

    (Universitat Autònoma de Barcelona (UAB))

  • Ahmed Eltawil

    (King Abdullah University of Science and Technology (KAUST))

  • Gianluca Setti

    (King Abdullah University of Science and Technology (KAUST))

  • Kamilya Smagulova

    (King Abdullah University of Science and Technology (KAUST))

  • Khaled N. Salama

    (King Abdullah University of Science and Technology (KAUST))

  • Olga Krestinskaya

    (King Abdullah University of Science and Technology (KAUST))

  • Xiaobing Yan

    (Hebei University)

  • Kah-Wee Ang

    (National University of Singapore (NUS))

  • Samarth Jain

    (National University of Singapore (NUS))

  • Sifan Li

    (National University of Singapore (NUS))

  • Osamah Alharbi

    (King Abdullah University of Science and Technology (KAUST))

  • Sebastian Pazos

    (King Abdullah University of Science and Technology (KAUST))

  • Mario Lanza

    (King Abdullah University of Science and Technology (KAUST))

Abstract

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Suggested Citation

  • Fernando Aguirre & Abu Sebastian & Manuel Gallo & Wenhao Song & Tong Wang & J. Joshua Yang & Wei Lu & Meng-Fan Chang & Daniele Ielmini & Yuchao Yang & Adnan Mehonic & Anthony Kenyon & Marco A. Villena, 2024. "Hardware implementation of memristor-based artificial neural networks," Nature Communications, Nature, vol. 15(1), pages 1-40, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45670-9
    DOI: 10.1038/s41467-024-45670-9
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    References listed on IDEAS

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    1. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    2. Max M. Shulaker & Gage Hills & Rebecca S. Park & Roger T. Howe & Krishna Saraswat & H.-S. Philip Wong & Subhasish Mitra, 2017. "Three-dimensional integration of nanotechnologies for computing and data storage on a single chip," Nature, Nature, vol. 547(7661), pages 74-78, July.
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