  Machine learning technologies have found fertile ground in optics due to its
promising features based on speed and parallelism. Feed-forward neural networks
are one of the most widely used machine learning algorithms due to their
simplicity and universal approximation capability. However, the typical
training procedure, where all weights are optimized, can be time and energy
consuming. An alternative approach is the Extreme Learning Machine, a
feed-forward neural network in which only the output weights are trained, while
the internal connections are random. Here we present an experimental
implementation of a photonic extreme learning machine (PELM) in an integrated
silicon chip. The PELM is based on the processing of the image of the scattered
light by an array of 18 gratings coupled to microresonators. Light propagation
in the microresonator array is a linear process while light detection by the
video camera is a nonlinear process. Training is done offline by analyzing the
recorded scattered light image with a linear classifier. We provide a
proof-of-concept demonstration of the PELM by solving both binary and analog
tasks, and show how the performance depends on the number of microresonators
used in the readout procedure.
