Supercontinuum-based photonic neural network for all-optical data classification

光子平台中的全光处理在能量效率和传输带宽方面优于当前的电子硬件。因此，它为即将到来的社会经济进步（如物联网、大数据网络以及金融市场的新型全球服务）所带来的速度和容量要求提供了解决方案。所要求的设备是一个高度可重构的超连续谱生成系统，它是实现一种新颖的、多功能的机器学习协处理器的不可或缺的工具包，用于以光速进行全光信息分类和路由。它将满足当今和未来电信基础设施的需求，以5G/6G和> 100 Gbit/s革命为代表。具体而言，该系统构成前馈型神经网络的光子硬件实现，使得能够处理存储在宽带脉冲的频谱相位和幅度中的信息。这是通过创建和互连频谱-时间模式（即，虚拟网络节点）通过超连续谱产生中涉及的复杂非线性光学过程。由此产生的，高光谱输出空间是很容易分离的广泛的信息分类任务。我们的系统旨在超越其他光子神经形态平台：1）处理速度，因为它不断适应传入的数据速率; 2）功耗，与海外电缆链路中使用的标准电信信号中继器相当; 3）学习能力。特别是，后者允许我们的神经网络处理器在训练系统实现多个高度多样化的任务方面具有非常多的功能。这些包括a）电信数据和报头识别，B）激光显微镜图像数据的单次在线特征识别，以及c）复杂电信网络的在线分散监测。在我们的建议中，我们将解决拟议网络的实施和培训问题，以及其基准问题。所要求的设备包括具有成本效益的现成光纤组件包（转化为器械再现性和实用性），即：1）用于信息编码的宽带可编程相位和幅度滤波器，以及2）色散补偿和高度非线性光纤模块（均保持偏振）用于超连续谱产生，以及3）可重新配置的激光器系统，以针对所处理的应用实现和基准测试全光相位编码。该捆绑包对于扩展当前的光学处理能力至关重要，其中PI的设施代表了最先进的技术水平，使用现成的电信设备来实现成本和功率效率高的设备。

All-optical processing in photonic platforms outperforms current electronic hardware in energy efficiency and transfer bandwidth. As such, it offers the solution to the speed and capacity requirements imposed by upcoming socio-economical advances such as the Internet of Things, Big Data networks, as well as novel global services for financial markets. The requested equipment a highly-reconfigurable supercontinuum generation system represents an indispensable toolkit to realize a novel, versatile machine learning co-processor for all-optical information classification and routing at the speed of light. It will serve the demands of today's and tomorrow's telecommunications infrastructure, represented by the 5G/6G and > 100 Gbit/s revolutions. Specifically, the system constitutes a photonic hardware implementation of a feed-forward-type neural network, enabling the processing of information stored in the spectral phases and amplitudes of broadband pulses. This is achieved by creating and interconnecting spectro-temporal modes (i.e., virtual network nodes) via the complex nonlinear-optical processes involved in supercontinuum generation. The resulting, hyperspectral output space is easily separable for a broad range of information-classification tasks. Our system aims to outperform other photonic neuromorphic platforms in 1) processing speeds, as it continuously adapts to the incoming data rate, 2) power consumption, comparable to that of standard telecom signal repeaters used in oversea cable links, and 3) learning capacity. In particular, the latter allows our neural network processor to be exceptionally versatile in training the system towards multiple, highly diverse tasks. These include a) telecom data and header recognition, b) single-shot in-line feature recognition of laser-microscope image data, and c) online dispersion monitoring of complex telecom networks. In our proposal, we will tackle the implementation and training of the proposed network, as well as its benchmarking. The requested equipment includes a cost-effective bundle of off-the-shelf fiber components (translating to device reproducibility and practicality), namely: 1) a broadband programmable phase and amplitude filter for information encoding, together with 2) dispersion compensating and highly nonlinear fiber modules (both polarization-maintaining) for supercontinuum generation, as well as 3) a reconfigurable laser system to implement and benchmark all-optical phase encoding for the tackled applications. The bundle is essential to extend current optical processing capabilities, where the PI's facilities represent the state-of-the-art, to cost- and power-efficient devices using off-the-shelf telecom equipment.