集成的神经形态光子计算用于人工智能加速:新兴设备,网络架构和未来范式
Gaofei Wang1,2,3, Junyan Che1,2,3, Chen Gao1,2,3
1College of Integrated Circuits & Micro-Nano Electronics, Fudan University, 220 Handan Road, Shanghai, 200433, P. R. China.
Advanced materials (Deerfield Beach, Fla.)
|October 21, 2025
概括
光子神经形态计算通过使用光来实现更快,更节能的计算,为AI硬件限制提供解决方案. 本综述详细介绍了光子神经网络 (PNN) 在下一代人工智能加速方面的进展.
科学领域:
- 这是光子神经形态计算.
- 人工智能 硬件加速 硬件加速
- 综合光子学 综合光子学
背景情况:
- 电子硬件面临物理限制 (晶体管缩放,·诺伊曼架构,散热),阻碍AI计算密度和能源效率.
- 深度学习,包括大型语言模型 (LLM),需要大量的计算资源,加剧了硬件瓶.
研究的目的:
- 审查光子神经网络 (PNN) 作为AI硬件限制的解决方案的十年进展.
- 分析核心PNN组件,网络架构以及云端和边缘AI的特定应用程序要求.
- 概述在PNN开发中克服物质和系统层面障碍的途径.
主要方法:
- 对线性突触装置,非线性神经元装置和PNN架构的进展进行系统审查和批判性分析.
- 分析PNN在云规模和边缘/客户端AI部署的特定应用程序要求.
- 识别材料和系统层面的障碍,并提出解决方案,包括拓优化的设备和先进的包装.
主要成果:
- 光子神经网络 (PNN) 显示了人工智能加速的潜力,实现了推理和现场训练的单芯片集成.
- 在核心PNN组件方面取得了重大进展,尽管仍存在挑战.
- PNN利用光的平行性,低延迟和最小的热损失来实现高效的矩阵操作.
结论:
- 光子神经形态计算,特别是PNN,为莫尔后的人工智能硬件带来了范式转变.
- 克服材料和包装方面的挑战对于广泛部署PNN至关重要.
- PNN为下一代节能AI加速提供了一个有前途的平台.
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