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Showing 1–11 of 11 results
Advanced filters: Author: A. Mehonic Clear advanced filters

  • Tony Kenyon, the director of the Neuroware Innovation and Knowledge Centre (IKC), introduces the UK’s first IKC in neuromorphic (brain-inspired) computing hardware — its goals, structure and the broader vision for brain-inspired technologies.

    • A. J. Kenyon
    • Rachel Won
    Comments & Opinion
    Nature Reviews Electrical Engineering
    Volume: 2, P: 709-710

  • Memristors hold promise for massively-parallel computing at low power. Aguirre et al. provide a comprehensive protocol of the materials and methods for designing memristive artificial neural networks with the detailed working principles of each building block and the tools for performance evaluation.

    • Fernando Aguirre
    • Abu Sebastian
    • Mario Lanza
    ReviewsOpen Access
    Nature Communications
    Volume: 15, P: 1-40

  • Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.

    • D. Joksas
    • P. Freitas
    • A. Mehonic
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-10

  • Electrophysical processes are used to create third-order nanoscale circuit elements, and these are used to realize a transistorless network that can perform Boolean operations and find solutions to a computationally hard graph-partitioning problem.

    • Suhas Kumar
    • R. Stanley Williams
    • Ziwen Wang
    Research
    Nature
    Volume: 585, P: 518-523

  • The benefits and future prospects of neuromorphic, or bio-inspired, computing technologies are discussed, as is the need for a global, coordinated approach to funding, research and collaboration.

    • A. Mehonic
    • A. J. Kenyon
    Reviews
    Nature
    Volume: 604, P: 255-260

  • Current physical neuromorphic computing faces critical challenges of how to reconfigure key physical dynamics of a system to adapt computational performance to match a diverse range of tasks. Here the authors present a task-adaptive approach to physical neuromorphic computing based on on-demand control of computing performance using various magnetic phases of chiral magnets.

    • Oscar Lee
    • Tianyi Wei
    • Hidekazu Kurebayashi
    ResearchOpen Access
    Nature Materials
    Volume: 23, P: 79-87

  • Dielectric breakdown is a major reliability issue in electronic devices. This Review discusses the data and knowledge accumulated from experimental and theoretical studies of dielectric breakdown in different insulating materials, with a focus on phenomenological models and novel computational approaches.

    • Andrea Padovani
    • Paolo La Torraca
    • Alexander L. Shluger
    Reviews
    Nature Reviews Materials
    Volume: 9, P: 607-627