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<p>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.</p>

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Neuromorphic computing at scale

  • 期刊论文

  • 发表日期：

    2025年1月22日

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  • 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

摘要

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.