Scalable reservoir computing on coherent linear photonic processor
Scalable reservoir computing on coherent linear photonic processor
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DOI：
10.1038/s42005-021-00519-1
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发表时间：
2021-02-10
影响因子：
5.5
通讯作者：
Hashimoto, Toshikazu
中科院分区：
文献类型：
--
作者：
Nakajima, Mitsumasa;Tanaka, Kenji;Hashimoto, Toshikazu
光子神经形态计算由于其超高计算速度和能量效率的巨大潜力而受到特别关注。光子计算硬件的优势在于其超宽带宽和利用固有并行性进行并行处理。在这里，我们展示了一个可扩展的片上光子实现的简化递归神经网络，称为水库计算机，使用集成相干线性光子处理器。与以前的方法相比，输入和循环权值都是通过光子线性处理在时空域中编码的，这使得超越输入电带宽的可扩展和超快速计算成为可能。由于该设备可以同时处理电信c波段的多个波长输入，我们可以使用超宽光带宽（类似于5太赫兹）作为计算资源。标准基准的实验表明，该方法在混沌时间序列预测和图像分类方面具有良好的性能。该器件被认为能够对每个波长进行每秒21.12兆次的乘法累积运算（MAC.s(-1)），并且可以通过波分复用在单个光子芯片上达到千万亿次的计算速度。我们的结果对传统的图灵-冯-诺伊曼机器是一个挑战，它们证实了光子神经形态处理在光子芯片上实现千兆位级神经形态超级计算的巨大潜力。光计算由于其大带宽和多路信号处理能力，有望实现高速、低能耗的信息处理。作者在集成光子处理器中提出了一种循环神经网络实现，该实现使用储层计算架构，能够对每个波长通道每秒执行类似于10 tera的乘法累积操作。
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 (similar to 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. Optical computing holds promise for high-speed, low-energy information processing due to its large bandwidth and ability to multiplex signals. The authors propose a recurrent neural network implementation using reservoir computing architecture in an integrated photonic processor capable of performing similar to 10 tera multiplication-accumulation operations per second for each wavelength channel.