研究目的
Investigating the architectures of reservoir computing and decision making based on complex photonics for photonic accelerators.
研究成果
Reservoir computing and decision making are promising functionalities of photonic accelerators, with potential applications in artificial intelligence. The study highlights the importance of laser dynamics and optical feedback in enhancing processing speed and decision-making efficiency.
研究不足
The study is limited by the technical constraints of photonic integrated circuits and the need for optimization in memory capacity and processing speed.
1：Experimental Design and Method Selection:
Utilizes semiconductor lasers with optical feedback for reservoir computing and decision making.
2：Sample Selection and Data Sources:
Uses chaotic temporal waveforms from semiconductor lasers.
3：List of Experimental Equipment and Materials:
Includes semiconductor lasers, photonic integrated circuits, and ring lasers on a chip.
4：Experimental Procedures and Operational Workflow:
Involves generating chaotic waveforms, applying mask signals, and analyzing outputs for decision making.
5：Data Analysis Methods:
Employs linear least-squares method for training output weights in reservoir computing.
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