Parallel photonic information processing at gigabyte per second data rates using transient states

Abstract

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Reservoir Computing Speeds Up

“…(11) have been performed in order to identify the optimal parameter conditions for best RC performances. These simulations allowed us to find qualitative criteria that are fully consistent with previously obtained results [5,6,9]. Quantitatively, excellent RC classification performances have been obtained both numerically and experimentally with WER close to 0%, thus confirming previous results from other photonic setups.…”

“…It is an attractive hardware solution, especially when photonic implementation is concerned, for which high-speed optical telecom devices can potentially provide unprecedented processing speed [6][7][8][9]. Delay dynamics are indeed known as having an infinite-dimensional phase space.…”

“…Very similar qualitative signal processing is obtained compared to the TI46 test, as can be observed in Figs. [8][9][10]. Despite the simplicity of our approach (single delayed feedback reservoir, dedicated analog hardware necessarily including processing noise and drifts, and moderate number of nodes compared to the advanced algorithmic RC approach in Ref.…”

Experimental demonstration of reservoir computing on a silicon photonics chip

“…This is particularly attractive when the information is already in the optical domain as in the case of many telecom and image processing applications. Optical reservoirs based on a fibre and one dynamical node [14][15][16][17][18] as well as reservoirs based on ring resonators 19 have been demonstrated. In our own previous work we have shown through reservoir simulations that integrated optical chips with a network of coupled semiconductor optical amplifiers can also be used, with the advantage of a much smaller footprint 20 .…”

Advances in photonic reservoir computing

“…Several practical implementations, either based on, e.g. semiconductor lasers [67], SOAs [23], or passive optical cavities [68] fall in this category. The number of virtual reservoir nodes in these hardware implementations is typically in the range 50-400.…”