Large-scale photonic natural language processing

TL;DR

This paper demonstrates a large-scale photonic processor capable of handling over 15 billion optical nodes, enabling efficient natural language processing with minimal training data by leveraging 3D optical field propagation.

Contribution

The authors develop a photonic network with unprecedented capacity, surpassing previous limits by over tenfold, facilitating large-scale text encoding and classification in photonic hardware.

Findings

Achieved a photonic processor with >1.5×10^{10} optical nodes.

Enabled high-performance NLP with minimal training data.

Overcome previous capacity limitations in photonic networks.

Abstract

Modern machine learning applications require huge artificial networks demanding in computational power and memory. Light-based platforms promise ultra-fast and energy-efficient hardware, which may help in realizing next-generation data processing devices. However, current photonic networks are limited by the number of input-output nodes that can be processed in a single shot. This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing. Here, we realize a photonic processor with a capacity exceeding $1.5 \times 10^{10}$ optical nodes, more than one order of magnitude larger than any previous implementation, which enables photonic large-scale text encoding and classification. By exploiting the full three-dimensional structure of the optical field propagating in free space, we overcome the interpolation threshold and reach the over-parametrized region of machine learning, a condition that allows high-performance natural language processing with a minimal fraction of training points. Our results provide a novel solution to scale-up light-driven computing and open the route to photonic language processing.