Neuromorphic computing at scale

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 Young, Chiara Bartolozzi, Amitava Majumdar, Suma George Cardwell, Melika Payvand, Sonia Buckley, Shruti Kulkarni, Hector A. Gonzalez, Gert Cauwenberghs, Chetan Singh Thakur, Anand Subramoney and Steve Furber
Additional contact information
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

Nature, 2025, vol. 637, issue 8047, 801-812

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41586-024-08253-8

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