A photonic reservoir computer (RC) leverages optical phenomena to implement multiplication by large pseudo-random matrices used by reservoir computers to perform complex machine learning tasks. Here we show that the equations for propagation around a multimode (MM) ring resonator can be cast exactly in the standard RC form with speckle mixing performing the matrix multiplication, an optical nonlinearity, and optical feedback. The hyperparameters are the outcoupling efficiency, the nonlinearity saturation level, and the input bias. The MM ring geometry reduces the sampling rate of backend ADCs by the number of neurons compared to single mode rings and removes the costly optical-to-electrical conversions required at each time step in the arrays. Simulations show a ring using a strongly guiding 50-m planar waveguide gives 200 neurons and excellent predictions and classifications of Mackey-Glass waveforms, while a weakly guiding MM 200-m diameter fiber gives about 4,000 neurons and excellent predictions of chaotic solutions of the Kuramoto-Sivashinsky equation. We perform several simulations of both systems to demonstrate the spatial sampling requirements for the output speckle patterns and that these ring resonator RCs are not excessively sensitive to tuning of the hyperparameters. Finally, we propose designs implementing the system as a chip-scale device or with discrete components and a MM optical fiber.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.