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Stochastic modeling of microcavity laser-based photonic reservoir computing: An information processing capacity
Juncheng Huang1, Tao Wang1, Kathy Lüdge2
1State Key Laboratory of Integrated Service Networks, School of Communications Engineering, Xidian University, Xi'an, 710071, China.
Summary
Photonic time-delay reservoir computing (TDRC) offers an alternative to traditional architectures for temporal processing. This study optimizes TDRC hardware design using stochastic modeling, improving real-time forecasting capabilities.
Area of Science:
- Photonics
- Neuromorphic Computing
- Information Theory
Background:
- Limitations of von Neumann architectures for temporal processing.
- Growing demand for efficient real-time forecasting.
- Potential of photonic time-delay reservoir computing (TDRC).
Purpose of the Study:
- Develop a stochastic modeling framework for semiconductor microcavity laser-based TDRC.
- Quantify the trade-off between linear memory and nonlinear computation using Information Processing Capacity (IPC).
- Optimize hardware design for enhanced photonic neuromorphic systems.
Main Methods:
- Stochastic modeling of semiconductor microcavity lasers.
- Systematic parameter sweeps to analyze performance.
- Evaluation using Mackey-Glass and Santa Fe time-series prediction tasks.
Main Results:
- Identified optimal virtual node spacing for TDRC.
- Observed resonant degradation at rational time constant (τ)/period (T) ratios.
- Demonstrated laser-size-dependent performance influenced by spontaneous emission coupling (β).
- Found that while smaller cavities increase nonlinearity, noise degrades prediction accuracy.
Conclusions:
- Bridged theoretical metrics (IPC) with practical hardware design considerations.
- Provided insights for optimizing photonic neuromorphic systems.
- Enabled enhanced real-time forecasting capabilities through TDRC optimization.
