# Classification of time-domain waveforms using a speckle-based optical reservoir computer

arXiv: https://arxiv.org/abs/1909.12489 · HTML: https://mlsanity.com/paper/1909.12489/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input](https://mlsanity.com/paper/2401.14371/) (2024)
- [Nonlinear Optical Data Transformer for Machine Learning](https://mlsanity.com/paper/2208.09398/) (2022)
- [Low-power multi-mode fiber projector overcomes shallow neural networks classifiers](https://mlsanity.com/paper/2210.04745/) (2022)

## Abstract (arXiv)

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 optical demonstration of an analog 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.