E2CDA: Type I: Collaborative Research: Nanophotonic Neuromorphic Computing

E2CDA：I 型：协作研究：纳米光子神经形态计算

• 批准号: 1740262
• 负责人: Paul Prucnal
• 金额: $ 33.4万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740262&HistoricalAwards=false
• 关键词: E2CDA; Type; Collaborative; Research; Nanophotonic

With this program, the National Science Foundation has challenged the scientific community to help solve the problem of energy efficient computing. Data centers around the world spend almost equal amount of overhead power in cooling and power conversion for every Watt of computation. As society's appetite for information continues to grow, data centers will eventually consume a significant portion of the world's electricity. Today, the entire information technology industry relies on digital electronics, i.e. electrons carrying digital bits, which impose a barrier for how efficient a computational task can be, regardless of how brilliantly software routines can be engineered. This project will explore the physics of lightwaves to craft a "photonic" processor that breaks that efficiency barrier. This interdisciplinary endeavor will attempt to generate foundational science which will support future innovation in the tech industry. To the end of help building the workforce for the future, the investigators will continue their commitments in mentoring graduate and undergraduate students, and support broadening participation of women, minorities and underrepresented groups through STEM outreach, and NSF REU sites.The approach unites neuromorphic (neural network-inspired) architectures, nanophotonic devices, and emerging fabrication platforms. The technical challenge requires vertical integration of new optical devices, logic units, and control software. It utilizes diverse expertise of faculty members in nanophotonic devices, photonic systems and computer engineering. Specifically, the key points of innovation will include: 1) ultra-efficient electro-optic modulators that will act as neurons at the nanoscale; 2) reconfigurable hardware interconnection fabric on-chip that uses light as information carrier; and 3) creation of control software and benchmarks that are required to orient the project within the broader field of efficient computing. A strong experimental thrust to design, build, and demonstrate these proposed systems will serve to reduce the risk of development of this technology and pave the way for other researchers to join the emerging field of nanophotonic neuromorphic computing.

通过这项计划，国家科学基金会向科学界提出了挑战，要求他们帮助解决节能计算的问题。对于每瓦的计算，世界各地的数据中心在冷却和电力转换方面花费的开销电力几乎相等。随着社会对信息的需求持续增长，数据中心最终将消耗世界上很大一部分电力。今天，整个信息技术行业依赖于数字电子，即携带数字比特的电子，这为计算任务的效率设置了障碍，无论软件例程可以设计得多么出色。这个项目将探索光波的物理学，以制造一种打破效率障碍的“光子”处理器。这一跨学科的努力将试图产生基础科学，这将支持科技行业未来的创新。为了帮助建设未来的劳动力队伍，调查人员将继续致力于指导研究生和本科生，并支持通过STEM外联和NSF REU网站扩大女性、少数族裔和代表性不足群体的参与。该方法将神经形态(神经网络启发)架构、纳米光电子设备和新兴制造平台结合在一起。这一技术挑战需要新的光学设备、逻辑单元和控制软件的垂直集成。它利用了纳米光子设备、光子系统和计算机工程方面的教职员工的不同专业知识。具体地说，创新的要点将包括：1)将充当纳米级神经元的超高效电光调制器；2)使用光作为信息载体的可重构硬件互连结构芯片；以及3)创建控制软件和基准，以将项目定位于更广泛的高效计算领域。设计、建造和演示这些拟议的系统的强大实验推力将有助于降低这项技术开发的风险，并为其他研究人员加入新兴的纳米光子神经形态计算领域铺平道路。

项目成果

Neuromorphic Photonics: Current Status and Challenges

神经形态光子学：现状和挑战

• DOI: 10.1109/ecoc48923.2020.9333362
• 发表时间: 2020
• 期刊: 2020 European Conference on Optical Communications (ECOC
• 作者: Prucnal, Paul R.;de Lima, Thomas Ferrieira;Huang, Chaoran;Marquez, Bicky A.;Shastri, Bhavin J.
• 通讯作者: Shastri, Bhavin J.

Neuromorphic Photonic Integrated Circuits

• DOI: 10.1109/jstqe.2018.2840448
• 发表时间: 2018-11-01
• 期刊: IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
• 影响因子: 4.9
• 作者: Peng, Hsuan-Tung;Nahmias, Mitchell A.;Prucnal, Paul R.
• 通讯作者: Prucnal, Paul R.

Roadmap on emerging hardware and technology for machine learning

• DOI: 10.1088/1361-6528/aba70f
• 发表时间: 2021-01-01
• 期刊: NANOTECHNOLOGY
• 影响因子: 3.5
• 作者: Berggren, Karl;Xia, Qiangfei;Raychowdhury, Arijit
• 通讯作者: Raychowdhury, Arijit

MD1.1 - Advances in Neuromorphic Silicon Photonics (Keynote)

MD1.1 - 神经形态硅光子学的进展（主题演讲）

• DOI: 10.1109/phosst.2019.8795028
• 发表时间: 2019
• 期刊: Photonics Society Summer Topical Meeting Series
• 作者: P. R. Prucnal, A. N.

Temporal Information Processing With an Integrated Laser Neuron

• DOI: 10.1109/jstqe.2019.2927582
• 发表时间: 2020
• 期刊: IEEE Journal of Selected Topics in Quantum Electronics
• 影响因子: 4.9
• 作者: Hsuan-Tung Peng;Gerasimos Angelatos;T. F. de Lima;M. Nahmias;A. Tait;S. Abbaslou;B. Shastri;P. Prucnal