Deep Photonic Reservoir Computer for Speech Recognition

TL;DR

This paper introduces a photonic deep reservoir computer designed for speech recognition, combining energy efficiency with high-speed processing, and demonstrating its potential for low-power neuromorphic hardware applications.

Contribution

It proposes a novel photonic-based deep reservoir computing architecture optimized for speech recognition, enhancing performance while simplifying practical implementation.

Findings

Effective on various speech recognition tasks

Achieves high-speed processing of high-dimensional audio signals

Supports low-power neuromorphic hardware development

Abstract

Speech recognition is a critical task in the field of artificial intelligence and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing, is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared to more resource-intensive machine learning algorithms. In this work we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep reservoir computing. We propose a photonic-based deep reservoir computer and evaluate its effectiveness on different speech recognition tasks. We show specific design choices that aim to simplify the practical implementation of a reservoir computer while simultaneously achieving high-speed processing of high-dimensional audio signals. Overall, with the present work we hope to help the advancement of low-power and high-performance neuromorphic hardware.