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扩大端到端在芯片上的光子神经网络推断的规模
Bo Wu1, Chaoran Huang2, Jialong Zhang1
1Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China.
Light, science & applications
|September 17, 2025
概括
部分连贯的深度光学神经网络 (PDONNs) 克服了光学计算中的扩展挑战. 这一新策略使得更深的网络和更大的输入大小能够实现节能,可扩展的光学神经网络.
科学领域:
- 光子学和人工智能的人工智能
- 集成光学 集成光学 集成光学
- 神经形态计算是一种神经形态计算.
背景情况:
- 光学神经网络在带宽和能源效率方面比电子网络具有优势.
- 扩展挑战包括由于激活函数级联能力较弱而导致的网络深度有限以及光学矩阵规模限制的输入大小.
研究的目的:
- 为芯片上光学神经网络提出一种新的扩展策略.
- 为了实现更大的网络深度和更大的输入大小,以增强光学推理能力.
主要方法:
- 引入了部分连贯的深度光学神经网络 (PDONNs),具有光电光非线性激活功能,以获得正净收益.
- 实现了卷积层,以快速减少维度,以增加输入大小的容纳.
- 利用部分连贯的光源来减少对专用激光器和连贯检测的依赖.
主要成果:
- 制造了一个具有最大输入大小 (64) 和迄今为止最深的网络深度的单体集成光学神经网络.
- 在时尚图像分类方面达到96%的准确性,在手写数字分类方面达到94%.
- 在部分连贯的照明下,证明了持续的性能,突出了强度和可访问性.
结论:
- PDONNs代表了可扩展光学神经网络设计的重大进步.
- 拟议的架构促进了节能,广泛可访问的光学计算.
- 这项工作为实用,大规模的光学推理系统铺平了道路.
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