Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing
This addresses the need for efficient, real-time processing in optical communication systems, though it appears incremental as it builds on existing reservoir computing methods with a new hardware implementation.
The paper tackled the problem of scaling neuromorphic computing for optical communication by using Fabry-Perot lasers to enable parallel reservoir computing, demonstrating improved classification performance with up to 8 longitudinal modes for signal equalization in 25 Gbaud systems.
We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of nodes increases and the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as to enhance classification performance.