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

扩展当前计算系统性能的边界需要颠覆性创新，以使下一代架构超越传统的所谓冯·诺伊曼范式。在这个项目中，将开发一种新的光子非冯·诺伊曼系统，推动基于自然的计算在效率、能力和适用性方面的发展。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，并直接影响机器学习（ML）在科学发现、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改善中的应用领域。预计更广泛的影响将渗透到集成光子学领域，在芯片级实现微梳的新功能，可以作为新的传感和通信平台的关键推动者。项目成果将为混合光子和电子界面带来新知识和颠覆性创新；使系统和架构超越冯·诺伊曼范式，从而影响未来半导体技术（FST）平台，这是国家战略重点；培养下一代工程师在这一领域不断创新。虽然基于自然的非冯·诺伊曼计算机器，如D-Wave的量子退加工机显示出了希望，但由于它们要求苛刻（例如，低温）的操作条件，显著的体积，相对较高的能耗，以及它们对组合优化问题的有限适用性，这些当前的机器还远远不够引人注目。只有当它们在解决现实世界问题的重要部分时，比最先进的冯·诺伊曼平台更有能力和效率时，它们才真正可行。该项目雄心勃勃且广阔的愿景是将这样一台机器付诸实践，这只能通过对设备、电路、算法和机器学习的融合研究来实现。纳米光子学是一个有前途的方向，可以通过光学频率微梳来催化所需的变革性进步，该微梳可以控制为大规模计算系统。将开发一个基于microcomb的非冯诺依曼系统，它可以加速各种ML算法。构建中型到大规模的系统原型需要开发用于学习系统的物理硬件，集成利用微梳的硅光子电路，以及共同设计利用该机器独特功能的新型ML算法。该项目将教育和培训下一代研究人员，让他们跳出自己学科的框框思考，向他们灌输跨越学科界限的兴奋，并让他们第一手了解在具有高社会影响的工程系统应用中取得进展的需要。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.