Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Jan;637(8047):801-812.
doi: 10.1038/s41586-024-08253-8. Epub 2025 Jan 22.

Neuromorphic computing at scale

Dhireesha Kudithipudi  1 , Catherine Schuman  2 , Craig M Vineyard  3 , Tej Pandit  4 , Cory Merkel  5 , Rajkumar Kubendran  6 , James B Aimone  3 , Garrick Orchard  7 , Christian Mayr  8 , Ryad Benosman  7 , Joe Hays  9 , Cliff Young  10 , Chiara Bartolozzi  11 , Amitava Majumdar  12 , Suma George Cardwell  3 , Melika Payvand  13 , Sonia Buckley  14 , Shruti Kulkarni  15 , Hector A Gonzalez  16 , Gert Cauwenberghs  12 , Chetan Singh Thakur  17 , Anand Subramoney  18 , Steve Furber  19
Affiliations
Review

Neuromorphic computing at scale

Dhireesha Kudithipudi et al. Nature. 2025 Jan.

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.

PubMed Disclaimer

Conflict of interest statement

Competing interests: The authors declare no competing interests.

Similar articles

See all similar articles

Cited by

References

    1. Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990). Original article launching the field of neuromorphic electronic systems engineering founded in the physics of computing. - DOI
    1. Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022). A discussion of the potential of neuromorphic computing to revolutionize information processing, with a focus on bringing together disparate research communities to provide them with the necessary financing and support. - PubMed - DOI
    1. Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018). An introduction to Loihi, a neuromorphic chip that models spiking neural networks in silicon and achieves more than three orders of magnitude better energy–delay product over conventional solvers. - DOI
    1. Furber, S. & Bogdan, P. (eds) SpiNNaker: A Spiking Neural Network Architecture (now publishers, 2020). A book that explores the development of SpiNNaker-1, a large-scale neuromorphic computing (1 million core) processor platform optimized for simulating spiking neural networks, which will make use of advanced technology features to achieve cutting-edge power consumption and scalability.
    1. NSF International Workshop on Large Scale Neuromorphic Computing. https://www.nuailab.com/workshop.html (2022).

LinkOut - more resources