| Authors: | K.T Vandoorne, J. Dambre, D. Verstraeten, B. Schrauwen, P. Bienstman | | Title: | Parallel reservoir computing using optical amplifiers | | Format: | International Journal | | Publication date: | 7/2011 | | Journal/Conference/Book: | IEEE Transactions on Neural Networks
| | Volume(Issue): | 22(9) p.1469-1481 | | DOI: | 10.1109/tnn.2011.2161771 | | Citations: | 179 (Dimensions.ai - last update: 17/11/2024)
66 (OpenCitations - last update: 3/5/2024)
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Abstract
Reservoir Computing, a computational paradigm inspired on neural systems, has become increasingly popular in recent years for solving a variety of complex recognition and classification problems. Thus far, most implementations have been software-based, limiting their speed and power efficiency. Integrated photonics offers the potential for a fast, power efficient and massively parallel hardware implementation. We have previously proposed a network of coupled semiconductor optical amplifiers as an interesting test case for such a hardware implementation. In the present paper, we investigate the important design parameters and the consequences of process variations through simulations. We use an isolated word recognition task with babble noise to evaluate the performance of the photonic reservoirs with respect to traditional software reservoir implementations, which are based on leaky hyperbolic tangent functions. Our results show that the use of coherent light in a well tuned reservoir architecture offers significant performance benefits. The most important design parameters are the delay and the phase shift in the system's physical connections. With optimized values for these parameters, coherent SOA reservoirs can achieve better results than traditional simulated reservoirs. We also show that process variations hardly degrade the performance, but amplifier noise can be detrimental. This effect must therefore be taken into account when designing SOA-based reservoir computing implementations. Related Research Topics
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