Frequency multiplexed optical extreme learning machine

Alessandro Lupo; Lorenz Butschek; Serge Massar

doi:10.1117/12.2593955

2 September 2021 Frequency multiplexed optical extreme learning machine

Alessandro Lupo, Lorenz Butschek, Serge Massar

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041N (2021) https://doi.org/10.1117/12.2593955
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We propose an optical implementation of an Extreme Learning Machine (ELM) inspired by frequency-multiplexing techniques previously employed for Reservoir Computing. The input layer of the ELM is encoded in the lines of a frequency comb and the hidden layer is generated by making comb lines interfere. Multiplication by output weights can be performed optically. This approach combines the potential high-speed, low-power and paral- lelization advantages of Optical Neural Networks with the cheap training (both in terms of speed and power) of ELMs, which do not require slow gradient descent and error backpropagation algorithms. We present preliminary experimental results compared with simulations.

Conference Presentation

Citation Download Citation

Alessandro Lupo, Lorenz Butschek, and Serge Massar "Frequency multiplexed optical extreme learning machine", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041N (2 September 2021); https://doi.org/10.1117/12.2593955

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