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Proceedings Paper

Optical reservoir computer using speckle in a multimode waveguide
Author(s): Uttam Paudel; Marta Luengo-Kovac; Thomas J. Shaw; George C. Valley
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Paper Abstract

Reservoir computing (RC) is a class of recurrent neural network that expands the dimensionality of a time-domain signal by mapping it into a higher-dimension space to capture and predict features of complex, non-linear temporal dynamics. Hardware level implementation of RC requires a reservoir with a large number of fixed nodes and the ability to activate and read the output weights of the neurons. As training is performed at a single output layer using simple linear regression techniques, RC is significantly simpler than other recurrent neural networks and thus provides a potentially faster learning framework with low training cost. Here, we report on an optical implementation of a reservoir computer using speckle generated in a multimode fiber (MMF). Neurons are activated by driving pixels of a spatial light modulator (SLM) with time domain waveforms and the output of the SLM is imaged onto the MMF. The MMF output is imaged onto a camera whose image is digitally processed and fed back into the fiber through the SLM. We demonstrate recovery of Mackey- Glass waveforms and classification of multi-frequency sinusoids using the speckle-based optical reservoir computer. As all the components used in the experiment can be readily mapped into an integrated photonic circuit our result demonstrates a framework for building a scalable, chip-scale, optical reservoir computer.

Paper Details

Date Published: 24 February 2020
PDF: 6 pages
Proc. SPIE 11299, AI and Optical Data Sciences, 1129909 (24 February 2020); doi: 10.1117/12.2543220
Show Author Affiliations
Uttam Paudel, The Aerospace Corp. (United States)
Marta Luengo-Kovac, The Aerospace Corp. (United States)
Thomas J. Shaw, The Aerospace Corp. (United States)
George C. Valley, The Aerospace Corp. (United States)


Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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