E2CDA: Type I: Collaborative Research: Energy Efficient Computing with Chip-Based Photonics

E2CDA：类型 I：协作研究：基于芯片的光子学的节能计算

• 批准号: 1640108
• 负责人: Alexander Gaeta
• 金额: $ 114.56万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640108&HistoricalAwards=false
• 关键词: E2CDA; Type; Collaborative; Research; Energy

Amidst today's data explosion, demand for computing power is accelerating, and the energy requirements for solving critical problems in science, engineering, business, and intelligent processing are increasing dramatically. In order to address this critical issue, the scientific and engineering community is beginning to explore new approaches to computing, such as mimicking the brain's structure or the dynamical behavior of coupled particles. However, implementing these approaches with conventional computer architectures is highly inefficient from both energy and computing standpoints. As a result, there is a pressing need to develop revolutionary computer architectures that overcome these severe roadblocks. An ambitious program is to be pursued within the framework of this project in which light, rather than electrons, is used to realize new computing paradigms with superior energy scalability. Specifically, computing architectures will be explored that exploit the wave nature of light (i.e., its amplitude and phase) harnessing the remarkable advancements over the past decade in nanofabrication of complex photonic chips with thousands of high-performance devices. These photonic platforms would have the potential to be computationally powerful, operate with unparalleled energy efficiency, and are scalable and highly reconfigurable.To fulfill this vision of energy efficient photonic computing, the proposed research efforts will focus on the following two types of photonic processors: (1) Ising Machine and (2) Neuromorphic Computing Machine. The unifying aspect of these photonic processors is that they consist of dynamic networks of coupled photonic units and rely on the wave nature of light to solve problems which has no analogy in electron-based computing systems. Beyond developing new types of optical processors, photonics technology will be developed as the cornerstone of future computer systems. A novel architecture will be explored that integrates photonic processors and interconnects with electronic memory and processors to maximize the benefits of each technology. Research will also be undertaken to map complete, challenging problems to combinations of photonic accelerators and electronic processors, and to determine how to best scale them to maximize full-system performance. By innovating at all levels of the system - from devices to architectures including systems, compilers, and algorithms - the project would aim to achieve advances that cannot be realized within any single field.

在当今的数据爆炸中，对计算能力的需求正在加速，解决科学、工程、商业和智能处理中关键问题的能源需求也在急剧增加。 为了解决这个关键问题，科学和工程界开始探索新的计算方法，例如模仿大脑的结构或耦合粒子的动力学行为。 然而，从能量和计算的角度来看，用传统的计算机架构实现这些方法是非常低效的。因此，迫切需要开发革命性的计算机架构来克服这些严重的障碍。一个雄心勃勃的计划是追求在这个项目的框架内，光，而不是电子，是用来实现新的计算模式与上级能源的可扩展性。具体来说，将探索利用光的波动性质的计算架构（即，它的振幅和相位）利用了过去十年在具有数千个高性能器件的复杂光子芯片的纳米制造中的显著进步。这些光子平台将具有强大的计算能力、无与伦比的能量效率、可扩展性和高度可重构性，为了实现这一节能光子计算的愿景，拟议的研究工作将集中在以下两种类型的光子处理器上：（1）伊辛机和（2）神经形态计算机。 这些光子处理器的统一方面是它们由耦合光子单元的动态网络组成，并依赖于光的波动性质来解决电子计算系统中无法比拟的问题。除了开发新型的光学处理器，光子学技术将被开发为未来计算机系统的基石。 将探索一种新的架构，将光子处理器和互连与电子存储器和处理器，以最大限度地发挥每种技术的优势。还将进行研究，将完整的、具有挑战性的问题映射到光子加速器和电子处理器的组合，并确定如何最好地扩展它们，以最大限度地提高整个系统的性能。 通过在系统的各个层面进行创新-从设备到架构，包括系统，编译器和算法-该项目旨在实现任何单一领域都无法实现的进步。

项目成果

Synchronization of coupled optical microresonators

• DOI: 10.1038/s41566-018-0261-x
• 发表时间: 2018-11-01
• 期刊: NATURE PHOTONICS
• 影响因子: 35
• 作者: Jang, Jae K.;Klenner, Alexander;Gaeta, Alexander L.
• 通讯作者: Gaeta, Alexander L.

Observation of Arnold Tongues in Coupled Soliton Kerr Frequency Combs

• DOI: 10.1103/physrevlett.123.153901
• 发表时间: 2019-10-09
• 期刊: PHYSICAL REVIEW LETTERS
• 影响因子: 8.6
• 作者: Jang, Jae K.;Ji, Xingchen;Gaeta, Alexander L.
• 通讯作者: Gaeta, Alexander L.

Silicon Chip-Based Quantum Random Number Generator

基于硅芯片的量子随机数发生器

• DOI: 10.1364/cleo_si.2017.sm1m.1
• 发表时间: 2017
• 期刊: Conference on Lasers and Electro-Optics
• 作者: Okawachi, Yoshitomo;Yu, Mengjie;Luke, Kevin;Carvalho, Daniel O.;Lipson, Michal;Gaeta, Alexander L.
• 通讯作者: Gaeta, Alexander L.

Photonic Switched Optically Connected Memory: An Approach to Address Memory Challenges in Deep Learning

• DOI: 10.1109/jlt.2020.2975976
• 发表时间: 2020-05
• 期刊: Journal of Lightwave Technology
• 影响因子: 4.7
• 作者: Ziyi Zhu;G. D. Guglielmo;Q. Cheng;M. Glick;Jihye Kwon;H. Guan;L. Carloni;K. Bergman

Photonic Ising Spin-Glass via Chip-Based Degenerate Kerr Oscillators

通过基于芯片的简并克尔振荡器的光子伊辛自旋玻璃

• DOI: 10.1364/cleo_si.2020.sm3l.2
• 发表时间: 2020
• 期刊: Conference on Lasers and Electro-Optics
• 作者: Okawachi, Yoshitomo;Yu, Mengjie;Jang, J. K.;Ji, Xingchen;Zhao, Yun;Lipson, Michal;Gaeta, Alexander L.
• 通讯作者: Gaeta, Alexander L.