Future developments in deep learning applications requiring large datasets will be limited by power and speed limitations of silicon based Von-Neumann computing architectures. Optical architectures provide a low power and high speed hardware alternative. Recent publications have suggested promising implementations of optical neural networks (ONNs), showing huge orders of magnitude efficiency and speed gains over current state of the art hardware alternatives. In this work, the transmission of the Fabry-Perot Interferometer (FPI) is proposed as a low power, low footprint activation function unit. Numerical simulations of optical CNNs using the FPI based activation functions show accuracies of 98% on the MNIST dataset. An investigation of possible physical implementation of the network shows that an ONN based on current tunable FPIs could be slowed by actuation delays, but rapidly developing optical hardware fabrication techniques could make an integrated approach using the proposed FPI setups a powerful solution for previously inaccessible deep learning applications.

中文翻译：

---

使用法布里-珀罗干涉仪实现光学深度神经网络

需要大型数据集的深度学习应用的未来发展将受到基于硅的冯诺依曼计算架构的功率和速度限制。光学架构提供了一种低功耗和高速的硬件替代方案。最近的出版物提出了光神经网络 (ONN) 的有希望的实现，与当前最先进的硬件替代方案相比，显示出巨大的数量级效率和速度增益。在这项工作中，法布里-珀罗干涉仪 (FPI) 的传输被提议为一种低功耗、低占用空间的激活功能单元。使用基于 FPI 的激活函数对光学 CNN 进行的数值模拟在 MNIST 数据集上显示出 98% 的准确率。