Image classification using delay-based optoelectronic reservoir computing

Philip Jacobson; Mizuki Shirao; Kerry Yu; Guan-Lin Su; Ming C. Wu

doi:10.1117/12.2578062

5 March 2021 Image classification using delay-based optoelectronic reservoir computing

Philip Jacobson, Mizuki Shirao, Kerry Yu, Guan-Lin Su, Ming C. Wu

Proceedings Volume 11703, AI and Optical Data Sciences II; 117031O (2021) https://doi.org/10.1117/12.2578062
Event: SPIE OPTO, 2021, Online Only

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Philip Jacobson, Mizuki Shirao, Kerry Yu, Guan-Lin Su, and Ming C. Wu "Image classification using delay-based optoelectronic reservoir computing", Proc. SPIE 11703, AI and Optical Data Sciences II, 117031O (5 March 2021); https://doi.org/10.1117/12.2578062

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