aiqbench analysis
This research presents a novel approach using semiconductor lasers to implement approximate reservoir computing, significantly lowering energy consumption per sample while maintaining prediction accuracy for chaotic time-series tasks. By optimizing quantization bits and sampling frequency within the photonic hardware, the study demonstrates that physical machine learning can achieve efficiency gains without sacrificing performance on complex dynamic systems.
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
arXiv:2607.23288v1 Announce Type: cross Abstract: Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data. The quantization of the response signal from the reservoir is required for the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high performance and low energy consumption. However, few studies have been reported to investigate the effect of bit quantization and sampling frequency. In this study, we introduce a concept of approximate reservoir computing with a semiconductor laser by quantizing the amplitude of node states in the reservoir and output weights. We evaluate the performance of a chaotic time-series prediction task and energy consumption per sample. We achieve significant reduction of energy consumption by optimizing the number of quantization bits, the sampling frequency, and the injection current of the semiconductor laser, while maintaining the prediction performance.
Abstract:Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data. The quantization of the response signal from the reservoir is required for the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high performance and low energy consumption. However, few studies have been reported to investigate the effect of bit quantization and sampling frequency. In this study, we introduce a concept of approximate reservoir computing with a semiconductor laser by quantizing the amplitude of node states in the reservoir and output weights. We evaluate the performance of a chaotic time-series prediction task and energy consumption per sample. We achieve significant reduction of energy consumption by optimizing the number of quantization bits, the sampling frequency, and the injection current of the semiconductor laser, while maintaining the prediction performance.
Optics (physics.optics); Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD)
arXiv:2607.23288 [physics.optics]
(or arXiv:2607.23288v1 [physics.optics] for this version)
https://doi.org/10.48550/arXiv.2607.23288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 240 words
