Image classification using delay-based optoelectronic reservoir computing

Reservoir computing has emerged as a lightweight, high-speed machine learning paradigm. We introduce a new optoelectronic reservoir computer for image recognition, in which input data is first pre-processed offline using two convolutional neural network layers with randomly initialized weights, generating a series of random feature maps. These random feature maps are then multiplied by a random mask matrix to generate input nodes, which are then passed to the reservoir computer. Using the MNIST dataset in simulation, we achieve performance in line with state-of-the-art convolutional neural networks (1% error), while potentially offering order-of-magnitude improvement in training speeds.