Photonic neuromorphic computing is of particular interest due to its significant potential for ultrahigh computing speed and energy efficiency. The advantage of photonic computing hardware lies in its ultrawide bandwidth and parallel processing utilizing inherent parallelism. Here, we demonstrate a scalable on-chip photonic implementation of a simplified recurrent neural network, called a reservoir computer, using an integrated coherent linear photonic processor. In contrast to previous approaches, both the input and recurrent weights are encoded in the spatiotemporal domain by photonic linear processing, which enables scalable and ultrafast computing beyond the input electrical bandwidth. As the device can process multiple wavelength inputs over the telecom C-band simultaneously, we can use ultrawide optical bandwidth (~5 terahertz) as a computational resource. Experiments for the standard benchmarks showed good performance for chaotic time-series forecasting and image classification. The device is considered to be able to perform 21.12 tera multiplication–accumulation operations per second (MAC ∙ s⁻¹) for each wavelength and can reach petascale computation speed on a single photonic chip by using wavelength division multiplexing. Our results are challenging for conventional Turing–von Neumann machines, and they confirm the great potential of photonic neuromorphic processing towards peta-scale neuromorphic super-computing on a photonic chip.

光子神经形态计算由于其超高速计算速度和能源效率的巨大潜力而​​特别受关注。光子计算硬件的优势在于其超宽带宽和利用固有并行性的并行处理。在这里，我们演示了使用集成相干线性光子处理器的简化的递归神经网络的可扩展片上光子实现，称为储层计算机。与以前的方法相比，输入权重和递归权重都是通过光子线性处理在时空域中编码的，这使得可扩展且超快速的计算超出了输入电带宽。由于该设备可以同时处理电信C波段上的多个波长输入，我们可以使用超宽光带宽（约5太赫兹）作为计算资源。标准基准的实验显示，在混沌时间序列预测和图像分类方面表现良好。该设备被认为能够每秒执行21.12 tera乘法-累加操作（MAC∙s^-1），并且可以通过波分复用在单个光子芯片上达到PB级计算速度。对于传统的Turing–von Neumann机器，我们的结果具有挑战性，并且他们证实了光子神经形态加工在光子芯片上实现peta级神经形态超级计算的巨大潜力。