Wave physics as an analog recurrent neural network.

Abstract:

:Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

journal_name

Sci Adv

journal_title

Science advances

authors

Hughes TW,Williamson IAD,Minkov M,Fan S

doi

10.1126/sciadv.aay6946

subject

Has Abstract

pub_date

2019-12-20 00:00:00

pages

eaay6946

issue

12

issn

2375-2548

pii

aay6946

journal_volume

5

pub_type

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