A Time-shared Photonic Reservoir Computer for Big Data Analytics

Dharanidhar Dang, Rabi Mahapatra

Information processing has reached the era of big data. Big data challenges are difficult to address with traditional Von Neumann or Turing approach. Hence implementation of new computational techniques is highly essential. Nanophotonics with its remarkable speed and multiplexing capability is a promising candidate for such implementations. This paper proposes a novel photonic computing system made-up of Mach-Zehnder interferometer and an optical fiber spool to emulate a powerful machine learning technique called reservoir computing. The proposed system is also integrated with a time-division-multiplexing circuit to facilitate parallel computation of multiple tasks which is first of its kind. The proposed design performs large-scale tasks like spoken digit recognition, channel equalization, and time-series prediction. Experimental results with standard photonic simulator demonstrate significant performance in terms of speed and accuracy compared to state of the art digital and software implementations.

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