多任务光子储备池计算：基于硅微环谐振器的波分复用并行计算
Multi-task Photonic Reservoir Computing: Wavelength Division Multiplexing for Parallel Computing with a Silicon Microring Resonator
AI总结：
该研究提出基于硅微环谐振器的时延光子储备池计算系统，结合时分与波分复用技术，在单芯片上实现多任务并行计算，性能与单任务前沿水平相当。
摘要：
如今，随着对更强计算资源的需求持续增长，各类替代型先进计算范式正受到广泛研究。研究人员已投入大量努力来突破传统冯·诺依曼架构的局限。在电子学领域，存内计算作为解决存储与计算处理器之间著名瓶颈的可行方案应运而生，该瓶颈会降低数据的有效吞吐量。在光子学领域，新的研究方案试图将计算处理器与存储器集成在单个器件中。光子学不仅可在空间和时间维度上复用数据流，还可在频率（即波长）维度上实现复用，这使其非常适合并行计算。在此，我们通过数值模拟验证了在单个光子芯片中利用时分复用与波分复用（WDM）同时处理四项独立任务的可行性，为我们的方案提供了概念验证。该系统是基于微环谐振器（MRR）的时延储备池计算（TDRC）系统。所处理的任务覆盖不同应用场景：时间序列预测、波形信号分类、无线信道均衡以及雷达信号预测。我们还测试了该系统同时计算最多10个相同任务实例的能力，展现出优异性能。通过对作为所研究神经网络方案中神经元的节点进行时分复用，系统的占用面积得以减小。波分复用用于实现波长通道的并行化，每个通道对应处理一项任务。通过调整每个光通道的输入功率和频率，我们可使每项任务的性能水平与专注于单任务运行的前沿研究报告中所提及的性能相当。
英文摘要：
Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solution to the infamous bottleneck between memory and computing processors, which reduces the effective throughput of data. In photonics, novel schemes attempt to collocate the computing processor and memory in a single device. Photonics offers the flexibility of multiplexing streams of data not only spatially and in time, but also in frequency or, equivalently, in wavelength, which makes it highly suitable for parallel computing. Here, we numerically show the use of time and wavelength division multiplexing (WDM) to solve four independent tasks at the same time in a single photonic chip, serving as a proof of concept for our proposal. The system is a time-delay reservoir computing (TDRC) based on a microring resonator (MRR). The addressed tasks cover different applications: Time-series prediction, waveform signal classification, wireless channel equalization, and radar signal prediction. The system is also tested for simultaneous computing of up to 10 instances of the same task, exhibiting excellent performance. The footprint of the system is reduced by using time-division multiplexing of the nodes that act as the neurons of the studied neural network scheme. WDM is used for the parallelization of wavelength channels, each addressing a single task. By adjusting the input power and frequency of each optical channel, we can achieve levels of performance for each of the tasks that are comparable to those quoted in state-of-the-art reports focusing on single-task operation...
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