An optically accelerated extreme learning machine using hot atomic vapors

TL;DR

This paper introduces an optical hardware platform using hot atomic vapors to accelerate extreme learning machines, demonstrating improved training speed and efficiency for image classification tasks.

Contribution

It presents a novel combination of hot atomic vapor nonlinearities with extreme learning machines, both numerically and experimentally, for faster optical machine learning.

Findings

Enhanced training performance on MNIST dataset

Demonstrated feasibility of hot atomic vapor for optical neural computation

Identified hyperparameters for further optimization

Abstract

Machine learning is becoming a widely used technique with a impressive growth due to the diversity of problem of societal interest where it can offer practical solutions. This increase of applications and required resources start to become limited by present day hardware technologies. Indeed, novel machine learning subjects such as large language models or high resolution image recognition raise the question of large computing time and energy cost of the required computation. In this context, optical platforms have been designed for several years with the goal of developing more efficient hardware for machine learning. Among different explored platforms, optical free-space propagation offers various advantages: parallelism, low energy cost and computational speed. Here, we present a new design combining the strong and tunable nonlinear properties of a light beam propagating through a hot atomic vapor with an Extreme Learning Machine model. We numerically and experimentally demonstrate the enhancement of the training using such free-space nonlinear propagation on a MNIST image classification task. We point out different experimental hyperparameters that can be further optimized to improve the accuracy of the platform.