Implementation of Optical Deep Neural Networks using the Fabry-Perot Interferometer

Abstract

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.

References in corpus (13)

• Striving for Simplicity: The All Convolutional Net
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
• Reinforcement Learning in a large scale photonic Recurrent Neural Network
• Large-Scale Optical Neural Networks based on Photoelectric Multiplication
• Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
• The Numerics of GANs
• Associative Long Short-Term Memory
• On Complex Valued Convolutional Neural Networks
• Random Walk Initialization for Training Very Deep Feedforward Networks
• Resolution and sensitivity of a Fabry-Perot interferometer with a photon-number-resolving detector
• Gated Orthogonal Recurrent Units: On Learning to Forget
• Kronecker Recurrent Units