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 Advjournal_title
Science advancesauthors
Hughes TW,Williamson IAD,Minkov M,Fan Sdoi
10.1126/sciadv.aay6946subject
Has Abstractpub_date
2019-12-20 00:00:00pages
eaay6946issue
12issn
2375-2548pii
aay6946journal_volume
5pub_type
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