Large-scale photonic natural language processing  

作　　者：CARLO M.VALENSISE IVANA GRECCO DAVIDE PIERANGELI CLAUDIO C 

机构地区：[1]Enrico Fermi Research Center(CREF),00184 Rome,Italy [2]Physics Department,Sapienza University of Rome,00185 Rome,Italy [3]Institute for Complex Systems,National Research Council(ISC-CNR),00185 Rome,Italy

出　　处：《Photonics Research》2022年第12期2846-2853,共8页光子学研究（英文版）

基　　金：Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi;Ministero dell’Universitàe della Ricerca(PRIN No.20177PSCKT)。

摘　　要：Modern machine-learning applications require huge artificial networks demanding computational power and memory.Light-based platforms promise ultrafast and energy-efficient hardware,which may help realize nextgeneration data processing devices.However,current photonic networks are limited by the number of inputoutput 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 for supervised learning with a capacity exceeding 1.5×10^(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-parameterized region of machine learning,a condition that allows high-performance sentiment analysis 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 natural language processing.

关 键 词：demanding OVERCOME EXCEEDING 

分 类 号：O439[机械工程—光学工程] TP391.1[理学—光学]