论文标题
使用集成衍射光学器件的光子卷积神经网络
Photonic convolutional neural networks using integrated diffractive optics
论文作者
论文摘要
随着光子综合电路的最新快速进步，已经证明可编程光子芯片可用于实施人工神经网络。卷积神经网络（CNN）是一类深入学习方法，在图像分类和语音处理等应用中已经非常成功。我们提出了一种使用集成星形耦合器的傅立叶变换属性实现光子CNN的体系结构。我们在计算机模拟中显示了使用MNIST数据集的高精度图像分类。我们还对光子CNN中的组件缺陷进行建模，并表明可以在可编程芯片中恢复性能降解。与当前的实现相比，我们提出的架构可以大大减少物理足迹，因为它利用了光学的自然优势，因此为综合光子深度学习处理器提供了可扩展的途径。
With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.
