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| 神经形态计算: 从脉冲神经网络到边缘部署 |
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俞诗航1,2, 易梦军1,3, 吴洲1,2, 申富饶1,3, 赵健4
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1.计算机软件新技术国家重点实验室(南京大学), 江苏 南京 210023;2.南京大学 计算机学院, 江苏 南京 210023;3.南京大学 人工智能学院, 江苏 南京 210023;4.南京大学 电子科学与工程学院, 江苏 南京 210023
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| 摘要: |
| 受生物神经系统启发, 神经形态计算的概念于20世纪80年代被提出, 旨在模拟生物大脑的结构和功能, 实现更高效、更具生物合理性的计算方式. 作为神经形态计算的代表模型, 脉冲神经网络(SNN)因其脉冲稀疏性, 事件驱动性、生物可解释性以及硬件契合性等优势, 在资源严格受限的边缘智能任务中得到了广泛应用. 针对脉冲神经网络的边缘部署情况进行梳理和汇总, 首先从脉冲神经网络模型自身的原理出发, 论述脉冲神经网络的高能效计算方式以及巨大的边缘部署潜力. 然后介绍当下常见的脉冲神经网络硬件实现工具链, 并重点对脉冲神经网络在各类神经形态硬件平台的部署情况做详细的整理与分析. 最后, 考虑到硬件故障行为已发展为当下研究中不可避免的问题, 对脉冲神经网络边缘部署时的故障与容错研究进行概述. 从软件模型原理到硬件平台实现, 全面系统地介绍神经形态计算的最新进展, 分析脉冲神经网络边缘部署时遇到的困难与挑战, 并针对这些挑战给出未来可能的解决方向. |
| 关键词: 神经网络 脉冲神经网络 神经形态计算 边缘智能 |
| DOI：10.13328/j.cnki.jos.007298 |
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| 基金项目:国家电网有限公司科技项目(5700-202319302A-1-1-ZN) |
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| Neuromorphic Computing: From Spiking Neural Network to Edge Deployment |
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YU Shi-Hang1,2, YI Meng-Jun1,3, WU Zhou1,2, SHEN Fu-Rao1,3, ZHAO Jian4
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1.State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023, China;2.School of Computer Science, Nanjing University, Nanjing 210023, China;3.School of Artificial Intelligence, Nanjing University, Nanjing 210023, China;4.School of Electronic and Engineering, Nanjing University, Nanjing 210023, China
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| Abstract: |
| Inspired by the biological nervous system, the concept of neuromorphic computing was introduced in the 1980s. It aims to mimic the structure and function of the biological brain to achieve more efficient and biologically plausible computation. As a representative model of neuromorphic computing, spiking neural networks (SNNs) have been widely employed in edge intelligence tasks with strict resource constraints due to their spike sparsity, event-driven operation, biological interpretability, and hardware compatibility. This study summarizes the edge deployment of SNNs. First, based on the principles of the SNN model itself, it discusses the energy-efficient computation of SNNs and their huge potential for edge deployment. Then, the currently common hardware implementation toolchain for SNNs is introduced, and a detailed summary and analysis of SNN deployment on various types of neuromorphic hardware platforms are provided. Finally, considering that hardware fault behavior has become an unavoidable issue in current research, an overview of fault and fault tolerance research during deploying SNNs at the edge is also presented. This study offers a comprehensive and systematic summary of recent advances in neuromorphic computing,ranging from software model principles to hardware platform implementation. Additionally, it analyzes the difficulties and challenges in the edge deployment of SNNs and points out possible solutions to these challenges. |
| Key words: neural network spiking neural network (SNN) neuromorphic computing edge intelligence |
