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

TL;DR

This paper proposes using Fabry-Perot Interferometers as low-power, high-speed activation functions in optical neural networks, demonstrating high accuracy on MNIST and potential for scalable, efficient deep learning hardware.

Contribution

It introduces a novel FPI-based activation function for optical neural networks and explores its potential for practical, high-speed, low-power deep learning hardware implementations.

Findings

Achieved 98% accuracy on MNIST with FPI-based optical CNNs.

Identified actuation delays as a challenge for physical implementation.

Highlighted rapid advancements in optical hardware fabrication techniques.

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.