Frequency Multiplexed Optical Extreme Learning Machine

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.