Implementation of Optical Deep Neural Networks using the Fabry-Perot Interferometer
This work addresses hardware bottlenecks for large-scale deep learning applications, offering a potential optical solution, though it is incremental as it builds on existing optical neural network research.
The paper tackles the power and speed limitations of silicon-based computing for deep learning by proposing the Fabry-Perot Interferometer as a low-power, compact activation function for optical neural networks, achieving 98% accuracy on MNIST in simulations.
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.