Nonlinear optical feature generator for machine learning
Modern machine learning models use an ever-increasing number of parameters to train (175 x 10(9) parameters for GPT-3) with large datasets to achieve better performance. Optical computing has been rediscovered as a potential solution for large-scale data processing, taking advantage of linear optical accelerators that perform operations at lower power consumption. However, to achieve efficient computing with light, it remains a challenge to create and control nonlinearity optically rather than electronically. In this study, a reservoir computing approach (RC) is investigated using a 14-mm waveguide in LiNbO3 on an insulator as an optical processor to validate the benefit of optical nonlinearity. Data are encoded on the spectrum of a femtosecond pulse, which is launched into the waveguide. The output of the waveguide is a nonlinear transform of the input, enabled by optical nonlinearities. We show experimentally that a simple digital linear classifier using the output spectrum of the waveguide increases the classification accuracy of several databases by similar to 10% compared to untransformed data. In comparison, a digital neural network (NN) with tens of thousands of parameters was required to achieve similar accuracy. With the ability to reduce the number of parameters by a factor of at least 20, an integrated optical RC approach can attain a performance on a par with a digital NN.
WOS:001083785700001
2023-10-01
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10
106104
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We acknowledge support for nanofabrication from the Scientific Center of Optical and Electron Microscopy (ScopeM) and from the cleanroom facilities at BRNC and FIRST at ETH Zurich. This work was supported by the Swiss National Science Foundation, Grant No. | 179099 |
Swiss National Science Foundation | 714837 |
European Union's Horizon 2020 Research and Innovation Program from the European Research Council | |