Supercontinuum-based photonic neural network for all-optical data classification

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All-optical processing in photonic platforms outperforms current electronic hardware in energy efficiency and transfer bandwidth. As such, it offers the solution to the speed and capacity requirements imposed by upcoming socio-economical advances such as the Internet of Things, Big Data networks, as well as novel global services for financial markets. The requested equipment a highly-reconfigurable supercontinuum generation system represents an indispensable toolkit to realize a novel, versatile machine learning co-processor for all-optical information classification and routing at the speed of light. It will serve the demands of today's and tomorrow's telecommunications infrastructure, represented by the 5G/6G and > 100 Gbit/s revolutions. Specifically, the system constitutes a photonic hardware implementation of a feed-forward-type neural network, enabling the processing of information stored in the spectral phases and amplitudes of broadband pulses. This is achieved by creating and interconnecting spectro-temporal modes (i.e., virtual network nodes) via the complex nonlinear-optical processes involved in supercontinuum generation. The resulting, hyperspectral output space is easily separable for a broad range of information-classification tasks. Our system aims to outperform other photonic neuromorphic platforms in 1) processing speeds, as it continuously adapts to the incoming data rate, 2) power consumption, comparable to that of standard telecom signal repeaters used in oversea cable links, and 3) learning capacity. In particular, the latter allows our neural network processor to be exceptionally versatile in training the system towards multiple, highly diverse tasks. These include a) telecom data and header recognition, b) single-shot in-line feature recognition of laser-microscope image data, and c) online dispersion monitoring of complex telecom networks. In our proposal, we will tackle the implementation and training of the proposed network, as well as its benchmarking. The requested equipment includes a cost-effective bundle of off-the-shelf fiber components (translating to device reproducibility and practicality), namely: 1) a broadband programmable phase and amplitude filter for information encoding, together with 2) dispersion compensating and highly nonlinear fiber modules (both polarization-maintaining) for supercontinuum generation, as well as 3) a reconfigurable laser system to implement and benchmark all-optical phase encoding for the tackled applications. The bundle is essential to extend current optical processing capabilities, where the PI's facilities represent the state-of-the-art, to cost- and power-efficient devices using off-the-shelf telecom equipment.