Photonic implementation of the input and reservoir layers for a reservoir computing system based on a single VCSEL with two Mach-Zehnder modulators

Abstract

Hardware implementation of reservoir computing (RC), which could reduce the power consumption of machine learning and significantly enhance data processing speed, holds the potential to develop the next generation of machine learning hardware devices and chips. Due to the existing solution only implementing reservoir layers, the information processing speed of photonics RC system are limited. In this paper, a photonic implementation of a VMM-RC system based on single Vertical Cavity Surface Emitting Laser (VCSEL) with two Mach Zehnder modulators (MZMs) has been proposed. Unlike previous work, both the input and reservoir layers are realized in the optical domain. Additionally, the impact of various mask signals, such as Two-level mask, Six-level mask, and chaos mask signal, employed in system, has been investigated. The system's performance improves with the use of more complex mask(t). The minimum Normalized mean square error (NMSE) can reach 0.0020 (0.0456) for Santa-Fe chaotic time series prediction in simulation (experiment), while the minimum Word Error Rate (WER) can 0.0677 for handwritten digits recognition numerically. The VMM-RC proposed is instrumental in advancing the development of photonic RC by overcoming the long-standing limitations of photonic RC systems in reservoir implementation. Linear matrix computing units (the input layer) and nonlinear computing units (the reservoir layer) are simultaneously implemented in the optical domain, significantly enhancing the information processing speed of photonic RC systems.