In this paper we propose and numerically study a neuromorphic computing scheme that applies delay-based reservoir computing in a laser system consisting of two mutually coupled phase modulated lasers. The scheme can be monolithic integrated in a straightforward manner and alleviates the need for external optical injection, as the data can be directly applied on the on-chip phase modulator placed between the two lasers. The scheme also offers the benefit of increasing the nodes compared to a reservoir computing system using either one laser under feedback or laser under feedback and optical injection. Numerical simulations assess the performance of the integrated reservoir computing system in dispersion compensation tasks in short-reach optical communication systems. We numerically demonstrate that the proposed platform can recover severely distorted 25 Gbaud PAM-4 signals for transmission distances exceeding 50km and outperform other competing delay-based reservoir computing systems relying on optical feedback. The proposed scheme, thanks to its compactness and simplicity, can play the role of a monolithic integrated hardware accelerator in a wide range of application requiring high speed real time processing.

中文翻译：

---

基于互注入调相半导体激光器作为整体集成硬件加速器的储层计算

在本文中，我们提出并数值研究了一种神经形态计算方案，该方案将基于延迟的储层计算应用于由两个相互耦合的相位调制激光器组成的激光器系统中。该方案可以以直接的方式进行单片集成，并减轻了对外部光学注入的需求，因为可以将数据直接应用于放置在两个激光器之间的芯片上相位调制器。与使用反馈下的激光或反馈下的激光和光学注入的储层计算系统相比，该方案还提供了增加节点的好处。数值模拟评估了集成油藏计算系统在短距离光通信系统中色散补偿任务中的性能。我们用数值方法证明，提出的平台可以恢复传输距离超过50 km的严重失真的25 Gbaud PAM-4信号，并且胜过依赖光反馈的其他竞争性基于延迟的储层计算系统。所提出的方案由于其紧凑性和简单性，可以在需要高速实时处理的广泛应用中扮演单片集成硬件加速器的角色。