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利用多重复合的超表面来实现全光衍射处理器的多任务学习

Sahar Behroozinia¹, Qing Gu^1,2

¹Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, 27695, USA.

Nanophotonics (Berlin, Germany)

|December 16, 2024

概括

研究人员使用光开发了多任务衍射神经网络 (DNN). 这些光学AI系统可以同时执行多个识别任务,提高AI平台的计算效率.

科学领域:

光学和光子学在光学和光子学.
人工智能的人工智能
机器学习机器学习

背景情况:

衍射神经网络 (DNN) 提供使用光的高速,低能耗计算.
目前的DNN通常是单一任务,限制了它们在统一人工智能系统中的应用.
需要灵活的DNN架构,能够处理多任务.

研究的目的:

开发和演示使用DNN的光学多任务识别.
为了利用极化和波长复杂化来实现并行任务执行.
探索多通道DNN的新型优化框架.

主要方法:

利用极化和光的自由度的波长.
构建的双通道DNN与双层级联的元表面.
采用了元原子库和一个端到端的联合优化框架.
在MNIST,FMNIST和KMNIST数据集上测试了性能.

主要成果:

在双任务分类和单任务DNN方面取得了可比的准确性.
在三项任务的并行识别中证明了满意的>80%的准确性.
联合优化框架显著提高了分类器的性能,而不是元原子图书馆设计.

结论:

使用DNN的光学多任务识别是通过利用光的自由度来实现的.
基于metasurface的DNN可以实现多个AI任务的高通量并行处理.
这项研究为先进的超薄光学计算系统铺平了道路.

关键词:

深度学习是一种深度学习.衍射神经网络是一种衍射神经网络.metasurface 地表的表面是什么

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