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在芯片上使用光电快捷连接的深度残留光子神经网络

Kaiyuan Wang, Zihao Tang, Yunlong Li

|January 31, 2025

概括

我们介绍了剩余光子神经网络 (Res-PNN) 架构,以训练更深层的网络. 光电快捷连接稳定训练,改善深度学习硬件加速.

科学领域:

光子学是一个光子学.
深度学习硬件加速硬件加速
人工智能的人工智能

背景情况:

芯片上的光子神经网络 (PNN) 显示了深度学习的前景.
由于梯度消失/爆炸问题,训练更深层次的PNN具有挑战性.
现有的架构难以处理复杂的推理任务.

研究的目的:

提出一种新的芯片内深度残留光子神经网络 (Res-PNN) 架构.
为了使更深层次的PNN能够进行稳定的培训,以增强深度学习应用程序.
在PNN培训中解决梯度消失和爆炸问题.

主要方法:

开发了一个芯片上的Res-PNN架构,利用光电快捷连接.
使用功率分割器,波长解复器和光电探测器实现了快捷连接.
通过光学重量层促进了直接梯度反向传播路径.

主要成果:

在CIFAR-10上达到88.4%的分类准确度,在CIFAR-100上达到80.3%.
与完全连接的PNN相比,Res-PNN提高了CIFAR-10精度3.2%和CIFAR-100精度11.3%.
证明了更深层次的PNN的稳定训练,减轻了梯度问题.

结论:

拟议的Res-PNN架构有效地训练了更深层的光子神经网络.
光电快捷连接对于稳定的梯度传播和提高精度至关重要.
在深度学习中,Res-PNN为硬件加速提供了重大进步.

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