Classification of time-domain waveforms using a speckle-based optical reservoir computer
Abstract
Reservoir computing is a recurrent machine learning framework that expands the dimensionality of a problem by mapping an input signal into a higher-dimension reservoir space that can capture and predict features of complex, non-linear temporal dynamics. Here, we report on a bulk electro-optical demonstration of a reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide. We demonstrate that the hardware can successfully perform a multivariate audio classification task performed using the Japanese vowel speakers public data set. We perform full wave optical calculations of this architecture implemented in a chip-scale platform using an SiO2 waveguide and demonstrate that it performs as well as a fully numerical implementation of reservoir computing. As all the optical components used in the experiment can be fabricated using a commercial photonic integrated circuit foundry, our result demonstrates a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 08, 2020
- Source ID
- 10.1364/oe.379264
Entities
People
- George C. Valley
- Jacob Pilawa
- Marta Luengo-kovac
- T. Justin Shaw
- Uttam Paudel
Organizations
- United States Air Force