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用于神经形态计算的光子复合技术

Yunping Bai¹, Xingyuan Xu¹, Mengxi Tan²

¹State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Nanophotonics (Berlin, Germany)

|December 5, 2024

概括

光学神经网络 (ONN) 利用光子集成来实现高速计算. 本综述探讨了ONN中的多重复合技术,强调了未来对性能增强的技术需求.

科学领域:

光电学和光子学的光电子和光子学.
人工智能和机器学习
计算机科学与工程计算机科学与工程

背景情况:

人工神经网络和光子集成的快速发展正在推动光学计算的创新.
光学神经网络 (ONN) 通过利用时间,波长和空间维度,为高平行性和数据吞吐量提供了潜力.

研究的目的:

审查使用各种光子复杂化策略的光学神经网络 (ONN) 的最新进展.
提供对在ONN中光子复杂化技术的进步所需的关键技术的展望.

主要方法:

探索光子复杂化技术,包括时间,波长和空间域方法.
分析这些复杂化方法如何在ONN中实现大规模互连和线性计算功能.

主要成果:

通过有效的光子多重复合来实现高平行性和数据吞吐量的ONN的演示.
识别各种光子复合策略,使先进的ONN架构成为可能.

结论:

光子复杂化是高性能光学神经网络的关键推动因素.
进一步开发混合复合技术对于ONN技术的未来至关重要.

关键词:

集成光学集成光学光学计算操作的操作一个光学神经网络.通过光子复杂化进行光子复杂化.

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