Despite ever increasing computational power, recognition and classification problems remain challenging to solve.
Recently, advances have been made by the introduction of the new concept of reservoir computing. This is a
methodology coming from the field of machine learning and neural networks that has been successfully used in several
pattern classification problems, like speech and image recognition. Thus far, most implementations have been in
software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware
implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. Moreover, a photonic
implementation offers the promise of massively parallel information processing with low power and high speed.
We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could
be used as a reservoir by comparing it to conventional software implementations using a benchmark speech recognition
task. In spite of the differences with classical reservoir models, the performance of our photonic reservoir is comparable
to that of conventional implementations and sometimes slightly better. As our implementation uses coherent light for
information processing, we find that phase tuning is crucial to obtain high performance.
In parallel we investigate the use of a network of photonic crystal cavities. The coupled mode theory (CMT) is used to
investigate these resonators. A new framework is designed to model networks of resonators and SOAs. The same
network topologies are used, but feedback is added to control the internal dynamics of the system. By adjusting the
readout weights of the network in a controlled manner, we can generate arbitrary periodic patterns.