Temporal optical neurons for serial deep learning

TL;DR

This paper introduces a novel serial optical neural network that processes high-speed temporal data directly, achieving over 90% accuracy in simulations and recognizing ASCII codes at 12 Gbps, advancing optical deep learning for serial data.

Contribution

The paper presents the first serial optical neural network design that directly processes high-speed temporal signals, differing from traditional parallel optical approaches.

Findings

Achieved over 90% classification accuracy in simulations.

Successfully recognized ASCII codes at 12 Gbps.

Demonstrated a pseudo-3-layer optical neural network prototype.

Abstract

Deep learning is able to functionally simulate the human brain and thus, it has attracted considerable interest. Optics-assisted deep learning is a promising approach to improve the forward-propagation speed and reduce the power consumption. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. By utilizing ultra-short coherent optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 90% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII (American Standard Code for Information Interchange) codes of English letters at a data rate of 12 Gbps. This concept represents a novel one-dimensional realization of artificial neural networks, enabling an efficient application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for the temporal signal processing.