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

最近基于脑启发的神经形态计算（NC）的机器学习方法在执行复杂信息处理任务方面的成功，推动了新的非常规计算方案（如递归神经网络（RNN）和基于RNN的水库计算（RC））的重要研究，这些方案能够实现并行数据处理，以克服传统顺序计算的局限性。特别地，将库分解成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允许实现物理RC，其中基于光学的RC平台由于“光速”传播、固有并行性、相对低的操作功率以及利用诸如偏振和波长的附加自由度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偏振，用于扩大水库，并与CMOS兼容电子器件互连，用于功率高效的电可重编程反馈。拟议的物理RC PIC预计将通过减少与远程计算机通信的需求来影响移动的应用，例如无人驾驶汽车（UAV）和机器人平台，从而避免延迟并延长电池寿命。为了在光子集成芯片（PIC）上实现利用偏振自由度的水库计算（RC）处理器，我们提出了以下目标：（1）数值和理论研究，旨在探索引入偏振作为新的自由度对RC效率的影响，这取决于具有提供动态控制的电子反馈元件的PIC的底层架构;（2）设计、制造和表征与外部电子反馈互连的硅PIC，允许所设计的结构;（3）对PIC系统进行实验测试，通过外部电子反馈实现可重编程RC任务，验证理论研究，并评估其性能的相关应用提供更高的精度和更低的能耗相比，国家的，快速原型和测试将在UCSD进行，全尺寸运行在AIM光子铸造厂进行。所提出的研究本质上是变革性的，因为它将：（i）极大地扩展RC在CMOS兼容PIC平台中的适用性的限制，（ii）对可重新编程性对诱导的储层动态和相应的性能误差的影响形成基本的理解，（iii）扩展RC和用于RC的光学自由度的当前概念（例如，极化）。该项目的更广泛的变革性影响来自于创建一个新的更快、更高效的RC PIC加速器，该加速器将影响无人机和机器人平台等移动的应用。该项目将为研究生和本科生提供科学培训，并作为与初中和高中开展外联、教育和合作的基础。不同种族，性别和经济背景的学生在科学，技术，工程和数学（STEM）的参与将通过正在进行的RET，REU和COSMOS活动继续进行。该计划将继续开发一个即插即用集成光子学教育套件（IPEK），并将其传播到其他机构，为美国大量不同出身和性别的学生以及劳动力人口实施动手课程。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.