11-09-2017, 04:41 PM
The increasing amount of data generated in different areas of science and technology require new and efficient processing techniques, which go beyond traditional concepts. In this paper, we study numerically the information processing capacities of semiconductor lasers subject to delayed optical feedback. Based on the recent concept of reservoir computing, we show that certain tasks, which are intrinsically difficult for traditional computers, can be efficiently addressed by such systems. The main advantages of this approach include the possibility of simple hardware implementation and low-cost warehousing and ultra fast processing speed. The experimental results corroborate the numerical predictions.
Semiconductor lasers subjected to delayed optical feedback have recently shown great potential in solving computationally hard tasks. Through the optical implementation of a neuro-inspired computational scheme called reservoir computing, based on the transient response to optical data injection, high processing speeds have been demonstrated. While previous efforts have focused on signal bandwidths limited by the relaxation oscillation frequency of the semiconductor laser, we numerically demonstrate that the much faster phase response makes significantly higher processing speeds reachable. In addition, this also leads to shorter external cavity lengths that facilitate future chip implementations. We have numerically compared our system in a chaotic task of predicting time series considering two different feedback configurations. The results show that a prediction error can be obtained below 4% when the data is processed at 0.25 GSamples / s. In addition, our knowledge of the phase dynamics of optical injection in a semiconductor laser also provides a clear understanding of system performance at different levels of pump current, even below the solitary laser threshold. Taking into account the spontaneous emission noise and the noise in the reading layer, we obtain good prediction performance at fast processing speeds for realistic values of noise force.
Semiconductor lasers subjected to delayed optical feedback have recently shown great potential in solving computationally hard tasks. Through the optical implementation of a neuro-inspired computational scheme called reservoir computing, based on the transient response to optical data injection, high processing speeds have been demonstrated. While previous efforts have focused on signal bandwidths limited by the relaxation oscillation frequency of the semiconductor laser, we numerically demonstrate that the much faster phase response makes significantly higher processing speeds reachable. In addition, this also leads to shorter external cavity lengths that facilitate future chip implementations. We have numerically compared our system in a chaotic task of predicting time series considering two different feedback configurations. The results show that a prediction error can be obtained below 4% when the data is processed at 0.25 GSamples / s. In addition, our knowledge of the phase dynamics of optical injection in a semiconductor laser also provides a clear understanding of system performance at different levels of pump current, even below the solitary laser threshold. Taking into account the spontaneous emission noise and the noise in the reading layer, we obtain good prediction performance at fast processing speeds for realistic values of noise force.