综合光学神经网络的跨领域知识蒸:朝着具有伪零射击能力的强大而紧的学习方向
Optics express
|December 19, 2025
概括
本研究引入了使用跨领域电光知识蒸进行芯片内光学神经网络 (ONN) 的新培训方法. 这种方法显著提高了规模有限的ONN的识别准确性,提高了它们的实际适用性.
科学领域:
- 光子学是指光子学的使用方法.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 芯片上的光学神经网络 (ONN) 为高速AI计算提供了潜力.
- 传统的ONN培训方法面临着局限性,特别是对于规模有限的硬件.
- 开发高效的培训框架对于实际的ONN部署至关重要.
研究的目的:
- 为 ONN 提出一个新的培训框架,跨领域电光知识蒸 (CDEOKD),用于 ONN.
- 为了提高基于Mach-Zehnder干扰仪 (MZI) 的规模有限的ONNs的性能.
- 评估拟议的培训方法的稳定性和实际可行性.
主要方法:
- 使用高性能数字神经网络作为教师模型来生成软标签.
- 利用知识蒸来指导学生的培训-ONN.
- 系统地使用时尚-MNIST数据集对各种规模的完全连接的基于MZI的ONN框架进行了评估.
主要成果:
- 在规模有限的ONN的识别准确性方面取得了显著和持续的改进.
- 与传统培训相比,基于10x10x2MZI的ONN的准确度增加了27.36%.
- 在相位偏差,MZI损失和4位重量量化方面表现出强大的稳定性.
结论:
- CDEOKD框架有效地提高了ONN的性能,特别是对于规模有限的硬件.
- 拟议的方法提高了ONN的稳定性和适用于实际光子硬件的适用性.
- 该框架使伪零射击学习能力成为可能,扩大了集成ONN的应用潜力.
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