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灵活的多模神经网络,用于完整的光学计算

Zeyu Deng¹, Zhangqi Dang¹, Ziyang Zhang¹

¹Laboratory of Photonic Integration, School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China.

|April 28, 2025

概括

这项研究引入了一种新的光子芯片,用于高效的计算,在虹膜数据集上达到91%的准确性. 该设备操纵光干扰用于神经网络操作,减少对电子设备的依赖.

科学领域:

光子学是一个光子学.
光学计算是指光学计算.
人工智能硬件硬件

背景情况:

光子集成电路 (PIC) 提供计算优势,但受到传统设计的限制,如马赫-泽恩德干扰仪 (MZIs) 和微环共振器 (MRR).
现有的PIC主要执行线性操作,需要电子元件来执行复杂的任务,如非线性激活和数据处理,这否定了光子的好处.
克服电子瓶对于实现PIC在先进计算中的全部潜力至关重要.

研究的目的:

提出和演示一种新的光子芯片架构,能够完全在光学领域执行复杂的神经网络计算.
通过实现光干扰的灵活操纵,克服传统PIC的局限性,以实现非线性处理.
为了减少对光子计算系统中电子元件的依赖.

主要方法:

一个光子芯片使用一个多模波导和两组电极用于数据加载和神经网络通过光干扰操纵塑造的神经网络.
使用遗传算法优化光干扰模式,实现神经网络训练的光学计算.
通过在成型过程中使用光学计算来绕过对梯度采集的需求.

主要成果:

一个光子芯片的演示,可以通过光学来执行神经网络计算.
使用拟议的光子芯片,在虹膜数据集上实现了91%的分类准确度.
通过光学计算方法成功绕过梯度获取问题.

结论:

开发的光子芯片架构有效地处理光学领域的神经网络计算.
这种方法显著减少了对电子处理的需求,解决了PIC中的"电子过载"问题.
这些发现为计算应用中更实用,更高效的光子集成电路铺平了道路.

关键词:

应用科学应用科学自然科学自然科学物理物理学物理

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