PIC: Hybrid Photonic-Electronic Reprogrammable Reservoir Computing with Polarization Modes-enhanced Dimensionality

最近，基于大脑启发的神经形态计算（NC）的机器学习方法成功执行复杂的信息处理任务，引发了新的非常规计算方案的重大研究，如循环神经网络（RNNs）和基于rnn的水库计算（RC），它们能够实现并行数据处理，以克服传统顺序计算的局限性。特别是，将储层分解为具有静态权重的内部网络和具有自适应和可训练权重的输出神经元层，可以实现物理RC，其中基于光学的RC平台由于“光速”传播、固有的并行性、相对较低的操作功率以及利用额外自由度（如偏振和波长）的可能性而具有吸引力。此外，片上光子集成电路（PIC）提供增强的光物质相互作用和模式极化，以扩大储存器，并与CMOS兼容电子器件互连，以实现高效的电可编程反馈。拟议的物理RC pic有望通过减少与远程计算机的通信需求来影响无人驾驶汽车（UAV）和机器人平台等移动应用，从而避免延迟并延长电池寿命。为了利用光子集成芯片（PIC）上的极化自由度实现储层计算（RC）处理器，我们提出了以下目标：(1)通过数值和理论研究，探讨在PIC底层架构下引入极化作为新的自由度对RC效率的影响，并利用电子反馈元件提供动态控制；(2)设计、制造和表征与外部电子反馈互连的硅PIC，并承认所设计的架构；(3)采用外部电子反馈对PIC系统进行实验测试，以实现可重编程RC任务，验证理论研究并评估其相关应用性能，提供更高的精度和更低的能耗。快速原型和测试将在加州大学圣地亚哥分校进行，并在AIM Photonics代工厂进行全尺寸运行。拟议的研究在本质上是革命性的，因为它将：(i)极大地扩展RC在CMOS兼容PIC平台中的适用性限制，（ii）对可重新编程性对诱导油藏动力学和相应性能误差的影响有一个基本的理解，（iii）扩展RC和用于RC的光学自由度的当前概念（例如，偏振）。该项目的变革性更广泛的影响来自于创建一个新的更快、更高效的RC PIC加速器，这将影响无人机和机器人平台等移动应用。该项目将为研究生和本科生提供科学培训，并作为拓展、教育和与初高中合作努力的基础。不同种族、性别和经济背景的学生参与科学、技术、工程和数学（STEM）将通过正在进行的RET、REU和COSMOS活动继续进行。该项目将继续开发一个即插即用的集成光子学教育工具包（IPEK），并将其传播到其他机构，为美国不同来源和性别的大量学生和劳动力提供实践课程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The recent success of Machine Learning methods based on brain-inspired Neuromorphic Computing (NC) to perform complex information processing tasks spiked significant research in new unconventional computational schemes such as Recurrent Neural Networks (RNNs) and RNN-based Reservoir Computing (RC) which are capable to implement parallel data processing to overcome limitations of conventional sequential computing. Particularly, decomposing the reservoir into an inner network with static weights and an output neurons layer with adaptive and trainable weights allows realization of physical RC where optical-based RC platforms are attractive due to the “speed-of-light” propagation, inherent parallelism, relatively low operation power, and the possibility to harness additional degrees of freedom such as polarization and wavelength. Furthermore, on-chip Photonic Integrated Circuit (PIC) offer enhanced light-matter interaction and modes polarization for enlarged reservoir, and interconnection with CMOS compatible electronics for power efficient electrical reprogrammable feedback. The proposed physical RC PICs are expected to impact mobile applications such as unmanned autonomous vehicles (UAV) and robotic platforms by reducing the need for communication with remote computers, thus avoiding latency and prolonging battery life.(technical description) To realize the reservoir computing (RC) processor utilizing the polarization degrees of freedom on a photonic integrated chip (PIC), we propose the following objectives: (1) numerical and theoretical study aiming to explore the effect of introducing polarization as a new degree of freedom on RC efficiency depending on the underlying architecture of PIC with the electronic feedback elements providing dynamics control; (2) design, fabricate and characterize silicon PIC interconnected with external electronic feedback, admitting the designed architectures; (3) experimentally test the PIC system with external electronic feedback to realize reprogrammable RC tasks, validate the theoretical study and evaluate its performance for relevant applications providing higher accuracy and lower energy consumption compared to state-of-the-art. Rapid prototyping and testing will be performed at UCSD with full scale runs performed at the AIM Photonics foundry. The proposed research is transformative in nature as it will: (i) greatly expand the limits of applicability of RC in CMOS compatible PIC platforms, (ii) develop a fundamental understanding on the effect of reprogrammability on the induced reservoir dynamics and the corresponding performance error, (iii) expand the current notions of both RC and the optical degrees of freedom employed for RC (e.g., polarization). The transformative broader impact of the project arises from the creation of a new much faster and more efficient RC PIC accelerator that will impact mobile applications such as UAV and robotic platforms. The project will provide scientific training for students at graduate and undergraduate levels as well as serve as a basis for outreach, education and collaborative efforts with middle and high schools. Engagement of students of diverse ethnicity, gender and economic backgrounds in Science, Technology, Engineering and Mathematics (STEM) will be continued via the ongoing RET, REU, and COSMOS activities. The program will continue developing a plug & play Integrated Photonics Education Kit (IPEK) and disseminate it to other institutions to implement hands-on classes for a large number of students with diverse origins and gender, and workforce population in the U.S.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.