Approximate reservoir computing with a semiconductor laser for reducing energy consumption
Photonic reservoir computing is a promising physical machine-learning approach for time-series prediction. Quantization of the reservoir response signal is an important consideration in the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high prediction performance and low energy consumption. However, few studies have investigated the effects of the number of quantization bits and sampling frequency on both prediction performance and energy consumption. In this study, we introduce the concept of approximate reservoir computing with a semiconductor laser by quantizing the reservoir node states and ou...
| Authors | Tatsuki Ito, Kazutaka Kanno, Satoshi Kawakami et al. |
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| Primary category | physics.optics |
| Published | 25 July 2026 |
| Last updated at source | 13 October 2026 |
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