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; Steve Furber

doi:10.1038/s41586-024-08253-8

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 CauwenberghsChetan Singh Thakur, Anand Subramoney, Steve Furber

Learning Systems

Research output: Contribution to journal › Review article › peer-review

2 Scopus citations

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.

Original language	English
Article number	4910
Pages (from-to)	801-812
Number of pages	12
Journal	Nature
Volume	637
Issue number	8047
DOIs	https://doi.org/10.1038/s41586-024-08253-8
State	Published - Jan 23 2025

Funding

We thank A. Kanaev of the National Science Foundation, who has supported the large-scale neuromorphic computing workshop under NSF project #2231027. Other grants supporting the effort are NSF grant #2317706, #2332744 and DOE ASCR. We appreciate the valuable guidance on scalability from M. Davies (Intel). We also thank members of the Neuromorphic AI Lab\u2014P. Helfer, A. Daram and V. Karia\u2014for helpful suggestions and feedback. L. Aimone provided support in editing. We acknowledge members of the neuromorphic computing community who contributed to decades of research progress in the field. Certain commercial products, suppliers and software are identified in this paper to foster understanding. This identification does not imply recommendation or endorsement by the authors or their institutions, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

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Cite this

Kudithipudi, D., Schuman, C., Vineyard, C. M., Pandit, T., Merkel, C., Kubendran, R., Aimone, J. B., Orchard, G., Mayr, C., Benosman, R., Hays, J., Young, C., Bartolozzi, C., Majumdar, A., Cardwell, S. G., Payvand, M., Buckley, S., Kulkarni, S., Gonzalez, H. A., ... Furber, S. (2025). Neuromorphic computing at scale. Nature, 637(8047), 801-812. Article 4910. https://doi.org/10.1038/s41586-024-08253-8

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title = "Neuromorphic computing at scale",

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.",

author = "Dhireesha Kudithipudi and Catherine Schuman and Vineyard, {Craig M.} and Tej Pandit and Cory Merkel and Rajkumar Kubendran and Aimone, {James B.} and Garrick Orchard and Christian Mayr and Ryad Benosman and Joe Hays and Cliff Young and Chiara Bartolozzi and Amitava Majumdar and Cardwell, {Suma George} and Melika Payvand and Sonia Buckley and Shruti Kulkarni and Gonzalez, {Hector A.} and Gert Cauwenberghs and Thakur, {Chetan Singh} and Anand Subramoney and Steve Furber",

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Kudithipudi, D, Schuman, C, Vineyard, CM, Pandit, T, Merkel, C, Kubendran, R, Aimone, JB, Orchard, G, Mayr, C, Benosman, R, Hays, J, Young, C, Bartolozzi, C, Majumdar, A, Cardwell, SG, Payvand, M, Buckley, S, Kulkarni, S, Gonzalez, HA, Cauwenberghs, G, Thakur, CS, Subramoney, A & Furber, S 2025, 'Neuromorphic computing at scale', Nature, vol. 637, no. 8047, 4910, pp. 801-812. https://doi.org/10.1038/s41586-024-08253-8

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AU - Bartolozzi, Chiara

AU - Majumdar, Amitava

AU - Cardwell, Suma George

AU - Payvand, Melika

AU - Buckley, Sonia

AU - Kulkarni, Shruti

AU - Gonzalez, Hector A.

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AU - Subramoney, Anand

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AB - 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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Neuromorphic computing at scale

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