Kudithipudi Dhireesha, Schuman Catherine, Vineyard Craig M, Pandit Tej, Merkel Cory, Kubendran Rajkumar, Aimone James B, Orchard Garrick, Mayr Christian, Benosman Ryad, Hays Joe, Young Cliff, Bartolozzi Chiara, Majumdar Amitava, Cardwell Suma George, Payvand Melika, Buckley Sonia, Kulkarni Shruti, Gonzalez Hector A, Cauwenberghs Gert, Thakur Chetan Singh, Subramoney Anand, Furber Steve
University of Texas at San Antonio, San Antonio, TX, USA.
University of Tennessee, Knoxville, TN, USA.
Nature. 2025 Jan;637(8047):801-812. doi: 10.1038/s41586-024-08253-8. Epub 2025 Jan 22.
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.
神经形态计算是一种受大脑启发的硬件和算法设计方法，可有效实现人工神经网络。神经形态设计师运用神经科学家发现的生物智能原理来设计高效的计算系统，这些系统通常用于有尺寸、重量和功率限制的应用场景。鉴于该研究领域正处于关键节点，规划未来大规模神经形态系统的发展方向至关重要。我们描述了创建可扩展神经形态架构的方法并确定了关键特征。我们讨论了可从扩展中受益的潜在应用以及需要解决的主要挑战。此外，我们审视了维持增长所需的全面生态系统以及扩展神经形态系统时面临的新机遇。我们的工作提炼了多个计算子领域的观点，为旨在推动前沿发展的神经形态计算研究人员和从业者提供指导。
