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Neuromorphic computing at scale

Author

Listed:
  • Dhireesha Kudithipudi

    (University of Texas at San Antonio)

  • Catherine Schuman

    (University of Tennessee)

  • Craig M. Vineyard

    (Sandia National Laboratories)

  • Tej Pandit

    (University of Texas at San Antonio)

  • Cory Merkel

    (Rochester Institute of Technology)

  • Rajkumar Kubendran

    (University of Pittsburgh)

  • James B. Aimone

    (Sandia National Laboratories)

  • Garrick Orchard

    (Intel Labs)

  • Christian Mayr

    (Technische Universität Dresden)

  • Ryad Benosman

    (Intel Labs)

  • Joe Hays

    (U.S. Naval Research Laboratory)

  • Cliff Young

    (Google DeepMind)

  • Chiara Bartolozzi

    (Italian Institute of Technology)

  • Amitava Majumdar

    (University of California, San Diego)

  • Suma George Cardwell

    (Sandia National Laboratories)

  • Melika Payvand

    (University of Zürich and ETH Zürich)

  • Sonia Buckley

    (National Institute of Standards and Technology)

  • Shruti Kulkarni

    (Oak Ridge National Laboratory)

  • Hector A. Gonzalez

    (SpiNNcloud Systems GmbH)

  • Gert Cauwenberghs

    (University of California, San Diego)

  • Chetan Singh Thakur

    (Indian Institute of Science)

  • Anand Subramoney

    (Royal Holloway, University of London)

  • Steve Furber

    (The University of Manchester)

Abstract

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficient computational systems, often for applications with size, weight and power constraints. With this research field at a critical juncture, it is crucial to chart the course for the development of future large-scale neuromorphic systems. We describe approaches for creating scalable neuromorphic architectures and identify key features. We discuss potential applications that can benefit from scaling and the main challenges that need to be addressed. Furthermore, we examine a comprehensive ecosystem necessary to sustain growth and the new opportunities that lie ahead when scaling neuromorphic systems. Our work distils ideas from several computing sub-fields, providing guidance to researchers and practitioners of neuromorphic computing who aim to push the frontier forward.

Suggested Citation

  • Dhireesha Kudithipudi & Catherine Schuman & Craig M. Vineyard & Tej Pandit & Cory Merkel & Rajkumar Kubendran & James B. Aimone & Garrick Orchard & Christian Mayr & Ryad Benosman & Joe Hays & Cliff Yo, 2025. "Neuromorphic computing at scale," Nature, Nature, vol. 637(8047), pages 801-812, January.
    • Handle: RePEc:nat:nature:v:637:y:2025:i:8047:d:10.1038_s41586-024-08253-8
    DOI: 10.1038/s41586-024-08253-8
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