Li Daxing, Zhang Kuo, Hu Xiaoyong, Feng Shuai
Opt Lett. 2024 Nov 1;49(21):6297-6300. doi: 10.1364/OL.540184.
Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.
光学神经网络（ONNs）由于其低延迟、高带宽和并行处理能力，在加速人工智能任务处理方面具有巨大潜力。光子晶体（PhCs）因其独特的光子带隙特性和对光波的精确控制，在集成光电子学中得到广泛应用。在本研究中，我们提出了一种基于光子晶体的光学可重构卷积核。该内核可以通过构建一个光子晶体权重库对权重执行卷积操作。该卷积内核在开发的光学卷积神经网络框架内表现出卓越的性能，成功实现了各种图像边缘处理任务。对于MNIST数据集，它实现了97.81%的盲识别准确率，对于Fashion-MNIST数据集，实现了80.31%的盲识别准确率。本研究不仅证明了基于光子晶体构建光学神经网络的可行性，据我们所知，还为光学计算的未来发展提供了新途径。
