Takano Kosuke, Sugano Chihiro, Inubushi Masanobu, Yoshimura Kazuyuki, Sunada Satoshi, Kanno Kazutaka, Uchida Atsushi
Opt Express. 2018 Oct 29;26(22):29424-29439. doi: 10.1364/OE.26.029424.
Photonic reservoir computing is a new paradigm for performing high-speed prediction and classification tasks in an efficient manner. The major challenge for the miniaturization of photonic reservoir computing is the need for the use of photonic integrated circuits. Herein, we experimentally demonstrate reservoir computing using a photonic integrated circuit with a semiconductor laser and a short external cavity. We propose a method to increase the number of virtual nodes in delayed feedback using short node intervals and outputs from multiple delay times. We perform time-series prediction and nonlinear channel equalization tasks using reservoir computing with the photonic integrated circuit. We show that the photonic integrated circuit with optical feedback outperforms the photonic integrated circuit without optical feedback for prediction tasks. To enhance the memory effect we feed past input signals in the current input data and demonstrate successful performance in an n-step-ahead prediction task.
光子储层计算是一种以高效方式执行高速预测和分类任务的新范式。光子储层计算小型化面临的主要挑战是需要使用光子集成电路。在此，我们通过实验展示了使用具有半导体激光器和短外腔的光子集成电路进行储层计算。我们提出了一种方法，利用短节点间隔和多个延迟时间的输出增加延迟反馈中虚拟节点的数量。我们使用光子集成电路的储层计算执行时间序列预测和非线性信道均衡任务。我们表明，具有光反馈的光子集成电路在预测任务方面优于没有光反馈的光子集成电路。为了增强记忆效应，我们将过去的输入信号输入当前输入数据，并在提前n步预测任务中展示了成功的性能。
