Neuromorphic computing at scale

Dhireesha Kudithipudi¹, Catherine Schuman², Craig M Vineyard³, Tej Pandit⁴, Cory Merkel⁵, Rajkumar Kubendran⁶, James B Aimone³, Garrick Orchard⁷, Christian Mayr⁸, Ryad Benosman⁷, Joe Hays⁹, Cliff Young¹⁰, Chiara Bartolozzi¹¹, Amitava Majumdar¹², Suma George Cardwell³, Melika Payvand¹³, Sonia Buckley¹⁴, Shruti Kulkarni¹⁵, Hector A Gonzalez¹⁶, Gert Cauwenberghs¹², Chetan Singh Thakur¹⁷, Anand Subramoney¹⁸, Steve Furber¹⁹

Affiliations

¹ University of Texas at San Antonio, San Antonio, TX, USA. dhireesha.kudithipudi@utsa.edu.
² University of Tennessee, Knoxville, TN, USA.
³ Sandia National Laboratories, Albuquerque, NM, USA.
⁴ University of Texas at San Antonio, San Antonio, TX, USA.
⁵ Rochester Institute of Technology, Rochester, NY, USA.
⁶ University of Pittsburgh, Pittsburgh, PA, USA.
⁷ Intel Labs, Santa Clara, CA, USA.
⁸ Technische Universität Dresden, Dresden, Germany.
⁹ U.S. Naval Research Laboratory, Washington, DC, USA.
¹⁰ Google DeepMind, Mountain View, CA, USA.
¹¹ Italian Institute of Technology, Genoa, Italy.
¹² University of California, San Diego, San Diego, CA, USA.
¹³ Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland.
¹⁴ National Institute of Standards and Technology, Boulder, CO, USA.
¹⁵ Oak Ridge National Laboratory, Oak Ridge, TN, USA.
¹⁶ SpiNNcloud Systems GmbH, Dresden, Germany.
¹⁷ Indian Institute of Science, Bengaluru, India.
¹⁸ Royal Holloway, University of London, Egham, UK.
¹⁹ The University of Manchester, Manchester, UK.

PMID: 39843589
DOI: 10.1038/s41586-024-08253-8

Review

Neuromorphic computing at scale

Dhireesha Kudithipudi et al. Nature. 2025 Jan.

. 2025 Jan;637(8047):801-812.

doi: 10.1038/s41586-024-08253-8. Epub 2025 Jan 22.

Authors

Affiliations

¹ University of Texas at San Antonio, San Antonio, TX, USA. dhireesha.kudithipudi@utsa.edu.
² University of Tennessee, Knoxville, TN, USA.
³ Sandia National Laboratories, Albuquerque, NM, USA.
⁴ University of Texas at San Antonio, San Antonio, TX, USA.
⁵ Rochester Institute of Technology, Rochester, NY, USA.
⁶ University of Pittsburgh, Pittsburgh, PA, USA.
⁷ Intel Labs, Santa Clara, CA, USA.
⁸ Technische Universität Dresden, Dresden, Germany.
⁹ U.S. Naval Research Laboratory, Washington, DC, USA.
¹⁰ Google DeepMind, Mountain View, CA, USA.
¹¹ Italian Institute of Technology, Genoa, Italy.
¹² University of California, San Diego, San Diego, CA, USA.
¹³ Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland.
¹⁴ National Institute of Standards and Technology, Boulder, CO, USA.
¹⁵ Oak Ridge National Laboratory, Oak Ridge, TN, USA.
¹⁶ SpiNNcloud Systems GmbH, Dresden, Germany.
¹⁷ Indian Institute of Science, Bengaluru, India.
¹⁸ Royal Holloway, University of London, Egham, UK.
¹⁹ The University of Manchester, Manchester, UK.

PMID: 39843589
DOI: 10.1038/s41586-024-08253-8

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.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

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).

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Neuromorphic computing at scale

Affiliations

Neuromorphic computing at scale

Authors

Affiliations

Abstract

Conflict of interest statement

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources