Delay systems subject to delayed optical feedback have recently shown great potential in solving computationally hard tasks. By implementing a neuro-inspired computational scheme relying on the transient response to optical data injection, high processing speeds have been demonstrated. However, reservoir computing systems based on delay dynamics discussed in the literature are designed by coupling many different stand-alone components which lead to bulky, lack of long-term stability, non-monolithic systems. Here we numerically investigate the possibility of implementing reservoir computing schemes based on semiconductor ring lasers. Semiconductor ring lasers are semiconductor lasers where the laser cavity consists of a ring-shaped waveguide. SRLs are highly integrable and scalable, making them ideal candidates for key components in photonic integrated circuits. SRLs can generate light in two counterpropagating directions between which bistability has been demonstrated. We demonstrate that two independent machine learning tasks , even with different nature of inputs with different input data signals can be simultaneously computed using a single photonic nonlinear node relying on the parallelism offered by photonics. We illustrate the performance on simultaneous chaotic time series prediction and a classification of the Nonlinear Channel Equalization. We take advantage of different directional modes to process individual tasks. Each directional mode processes one individual task to mitigate possible crosstalk between the tasks. Our results indicate that prediction/classification with errors comparable to the state-of-the-art performance can be obtained even with noise despite the two tasks being computed simultaneously. We also find that a good performance is obtained for both tasks for a broad range of the parameters. The results are discussed in detail in [Nguimdo et al., IEEE Trans. Neural Netw. Learn. Syst. 26, pp. 3301–3307, 2015]
We investigate different scenarios leading to simultaneous time-delay concealment both in the intensity and the phase dynamics generated from semiconductor ring lasers (SRLs) subject to delayed feedback. Under appropriate conditions, we found that the delay signature can be eliminated both in the intensity and the phase dynamics of SRLs with cross-feedback even when subject to long delayed feedback. For SRLs with self-feedback configuration, we also found that the concealment of short delay time is possible. The fact that such delay signatures can be eliminated in SRLs subject to short feedback opens the possibility of implementing secure communication schemes and random number generators on chip.
Delay systems subject to delayed optical feedback have recently shown great potential in solving computationally
hard tasks. By implementing a neuro-inspired computational scheme relying on the transient response to optical
data injection, high processing speeds have been demonstrated. However, reservoir computing systems based on
delay dynamics discussed in the literature are designed by coupling many different stand-alone components which
lead to bulky, lack of long-term stability, non-monolithic systems. Here we numerically investigate the possibility
of implementing reservoir computing schemes based on semiconductor ring lasers as they are scalable and can
be easily implemented on chip. We numerically benchmark our system on a chaotic time-series prediction task.
We model the performance of an optoelectronic phase-chaos system operating with telecom components to generate random bits. The key component of the system is differential delay, namely the system is subject to two delay times which differ in an amount much larger than the autocorrelation time. This is implemented by a delay loop and an imbalanced Mach-Zhender modulator. We show that after suitable digitalization of the chaotic signal the generated bits pass all the NIST test for randomness. We also show that the system can be extended to have several chains in parallel each with a Mach-Zhender modulator, each chain being used to produce a sequence of random bits. If the differential delays of the Mach-Zhenders differ by an amount larger than the autocorrelation time of the chaotic dynamics, the output of the different chains is uncorrelated and therefore can be used for parallel generation of statistically independent random bit-streams. In addition, we also find that a sequence constructed by interleaving the parallel bit-streams also pass all the NIST tests for randomness. Based on the least significant bits which can be included in the sequence and the number of the parallel branches which can be implemented, we show that bit rates up to Tb/s can be achieved.
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