5 papers
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
Tatsuki Ito, Kazutaka Kanno, Satoshi Kawakami +1
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 requi…
Photonic decision making using optical frequency difference detection in mutually-coupled semiconductor lasers
Hidetoshi Taira, Takatomo Mihana, Shun Kotoku +4
As electronic computing approaches its performance limits, photonic accelerators have emerged as promising alternatives. Photonic accelerators exploiting semiconductor-laser synchr…
Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator
Felix Köster, Kazutaka Kanno, Atsushi Uchida
Reservoir computing has proven effective for tasks such as time-series prediction, particularly in the context of chaotic systems. However, conventional reservoir computing framewo…
Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
Satoshi Sunada, Tomoaki Niiyama, Kazutaka Kanno +4
The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical n…
Attention-Enhanced Reservoir Computing
Felix Köster, Kazutaka Kanno, Jun Ohkubo +1
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series rema…