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Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing

Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness

Photonic reservoir computing: a new approach to optical information processing

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 and has been successfully used in several pattern classification problems, like speech and image recognition. The implementations have so far 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. 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 on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance