ASCENT: Using Optical Frequency Comb for Ultrafast Nature-Based Computing for Machine Learning Algorithms

ASCENT：使用光学频率梳进行机器学习算法的超快基于自然的计算

• 批准号: 2231036
• 负责人: Michael Huang
• 金额: $ 149.99万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231036&HistoricalAwards=false
• 关键词: ASCENT; Using; Optical; Frequency; Comb

Expanding the boundaries of current computing system performance calls for disruptive innovations to enable next-generation architectures beyond the traditional, so-called von Neumann paradigm. In this project, a novel photonic non-von Neumann system will be developed that pushes the envelope of nature-based computing in efficiency, capability, and applicability. Products and insights from this ASCENT collaboration have strong transformative potentials to bring nature-based computing to the state of compelling infrastructure and directly impact the gamut of application domains of machine learning (ML) in scientific discovery, industry, assistive technologies, robotics-aided healthcare, economic development, and consequent improvements in quality of life. Envisioned broader impacts will permeate to the integrated photonics community, with new functions being realized at the chip level for microcombs that can serve as key enablers in new sensing and communication platforms. Project outcomes will generate new knowledge and disruptive innovation for hybrid photonic and electronic interfaces; enable systems and architectures beyond the von Neumann paradigm, and thus impact Future Semiconductor Technology (FST) platforms -- a strategic national priority; and train next generation engineers for continued innovation in this area.While nature-based, non-von Neumann computing machines such as D-Wave’s quantum annealers are showing promise, these current machines are far from compelling due to their demanding (e.g., cryogenic) operating conditions, significant bulk, their relatively high energy consumption, and their limited applicability to combinatorial optimization problems. They will only be truly viable when they are significantly more capable and efficient than state-of-the-art von Neumann platforms in solving a non-trivial section of real-world problems. This project’s ambitious and broad vision is to bring such a machine to fruition, which can only be realized via convergent research in devices, circuits, algorithms, and ML. Nanophotonics is a promising direction to catalyze the required transformative advances, through an optical frequency microcomb that can be controlled to function as a large-scale computing system. A microcomb-based non-von Neumann system will be developed, which can accelerate a variety of ML algorithms. Building medium- to large-scale system prototypes calls for developments in physical hardware for learning systems, integration of silicon photonic circuits exploiting microcombs, and co-design of novel ML algorithms that leverage the unique features of this machine. This project will educate and train the next generation of researchers to think outside the box of their niche discipline, instill in them the excitement of crossing disciplinary boundaries, and give them first-hand appreciation of the need for convergent efforts towards making progress in engineering system applications with high societal impact.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.

扩大当前计算系统性能的界限需要颠覆性创新，使下一代架构能够超越传统，即所谓的冯诺依曼范式。在该项目中，将开发一种新颖的光子非冯诺依曼系统，突破自然的极限。此次 ASCENT 合作的产品和见解具有强大的变革潜力，可以将基于自然的计算带入引人注目的基础设施状态，并直接影响科学领域机器学习 (ML) 的应用领域。发现、工业、辅助技术、机器人辅助医疗保健、经济发展以及随之而来的生活质量的改善预计将渗透到集成光子学领域，微梳的新功能将在芯片级实现，这将成为关键。新传感和通信平台的推动者将为混合光子和电子接口带来新的知识和颠覆性创新；使系统和架构超越冯诺依曼范式，从而影响未来半导体技术（FST）平台——国家战略优先事项；并培训下一代工程师以在该领域持续创新。虽然基于自然的非冯·诺依曼计算机（例如 D-Wave 的量子退火机）显示出了希望，但由于这些当前的计算机还远未引人注目。由于其苛刻的（例如低温）操作条件、巨大的体积、相对较高的能耗以及对组合优化问题的有限适用性，只有当它们的能力和效率显着高于现有技术时，它们才真正可行。艺术冯诺依曼平台解决现实世界问题的重要部分，该项目雄心勃勃且广阔的愿景是实现这样的机器，这只能通过设备、电路、算法和机器学习的融合研究来实现。催化所需变革的一个有前途的方向是通过可控制的光频微梳来实现大规模计算系统的进展，将开发基于微梳的非冯·诺依曼系统，该系统可以加速各种技术的发展。机器学习算法。构建中型到大型系统原型需要开发学习系统的物理硬件、利用微梳的硅光子电路的集成以及利用该机器的独特功能的新颖机器学习算法的共同设计。教育和培训下一代研究人员跳出其利基学科的框框进行思考，向他们灌输跨越学科边界的兴奋感，并让他们亲身体会到需要共同努力，以在高水平的工程系统应用方面取得进展。社会影响。该奖项反映了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，NSF 的法定使命被认为值得支持。

项目成果

SUPPORTING ENERGY-BASED LEARNING WITH AN ISING MACHINE SUBSTRATE: A CASE STUDY ON RBM

使用 ISING 机器基板支持基于能量的学习：RBM 案例研究

• DOI: 10.1145/3613424.3614315
• 发表时间: 2023-10
• 期刊: ACM
• 作者: Vengalam, Uday Kumar;Liu, Yongchao;Geng, Tong;Wu, Hui;Huang, Michael
• 通讯作者: Huang, Michael