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

PIC：具有偏振模式增强维数的混合光子-电子可重编程储层计算

基本信息

批准号：
2217453
负责人：
Yeshaiahu Fainman
金额：
$ 42万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-15 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217453&HistoricalAwards=false
关键词：
PIC Hybrid Photonic Electronic Reprogrammable

项目摘要

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.

最近，基于类脑神经形态计算 (NC) 的机器学习方法在执行复杂的信息处理任务方面取得了成功，这引发了对新的非常规计算方案的重大研究，例如循环神经网络 (RNN) 和基于 RNN 的储层计算 (RC)，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，特别是，将存储库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 平台因其“光速”传播、固有的并行性、相对较低的操作功率以及利用偏振和波长等额外自由度的可能性而颇具吸引力，此外还有片上光子集成电路。 (PIC) 提供增强的光与物质相互作用和模式偏振，以扩大储层，并与 CMOS 兼容电子器件互连，以实现高能效的电气可重新编程反馈。所提出的物理 RC PIC 预计将影响无人驾驶汽车等移动应用。通过减少与远程计算机的通信需求，从而避免延迟并延长电池寿命，无人机（UAV）和机器人平台。（技术描述）利用光子集成芯片（PIC）上的偏振自由度实现储层计算（RC）处理器) )，我们提出以下目标：(1) 数值和理论研究，旨在探索引入极化作为新的自由度对 RC 效率的影响，具体取决于 PIC 的底层架构以及提供动态控制的电子反馈元件；( 2）设计、制造和表征与外部电子反馈互连的硅PIC，承认所设计的架构；（3）通过实验测试具有外部电子反馈的PIC系统以实现可重新编程的RC任务，验证理论研究并评估其为相关应用提供的性能，从而提供更高的精度和更低的能耗。快速原型设计和测试将在 UCSD 进行，并在 AIM Photonics 代工厂进行全面运行。拟议的研究本质上是变革性的，因为它将：（i）极大地扩展了技术的极限。 RC 在 CMOS 兼容 PIC 平台中的适用性，(ii) 对可重编程性对诱发储层动力学和相应性能误差的影响有一个基本的了解，(iii) 扩展了 RC 和光学自由度的当前概念RC（例如，极化）。该项目的变革性更广泛影响来自于创建更快、更高效的新 RC PIC 加速器，该加速器将影响无人机和机器人平台等移动应用。为研究生和本科生提供培训，并作为与初中和高中的外展、教育和合作的基础，让不同种族、性别和经济背景的学生参与科学、技术、工程和数学 (STEM) 领域。将通过正在进行的 RET、REU 和 COSMOS 活动继续该计划将继续开发即插即用集成光子学教育套件 (IPEK) 并将其分发给其他机构，为大量不同背景的学生实施实践课程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Vertical-cavity surface-emitting phase shifter

垂直腔面发射移相器

DOI：
10.1364/oe.497606
发表时间：
2023
期刊：
Optics Express
影响因子：
3.8
作者：
Almutairi, Dhaifallah;Johnson, Karl;Smolyaninov, Alexei;Grieco, Andrew;Fainman, Yeshaiahu
通讯作者：
Fainman, Yeshaiahu

Compensation of Kerr-induced impairments in silicon nitride third-harmonic generators

氮化硅三次谐波发生器中克尔引起的损伤的补偿