  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.
