ASCENT: Collaborative Research: Programmable Photonic Computation Accelerators (PPCA)

ASCENT：协作研究：可编程光子计算加速器（PPCA）

• 批准号: 2023730
• 负责人: Yeshaiahu Fainman
• 金额: $ 45万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023730&HistoricalAwards=false
• 关键词: ASCENT; Collaborative; Research; Programmable; Photonic

As the Fourth Industrial Revolution is approaching, large-scale computing is becoming more demanding and popular than ever. However, the performance of conventional electronic microprocessors has almost reached their limits for device speed, on-chip density and power consumption and will not be able to continue sustaining the upcoming data explosion. Optical computation can be extremely fast and with low-power requirements compared to electronics, for its intrinsic high speed, large bandwidth, and unlimited parallelism, which are critical to ease the data traffic associated with applications where artificial intelligence decisions need to be made in real time. Novel approaches towards programmable computations are required for data-driven training of modern artificial intelligence. In this project, the investigators will leverage the state-of-the-art integrated photonics technology to develop an innovative programmable photonic computation accelerators (PPCA), accelerating the computation speed and reducing the cost and energy consumption to sustain long term performance requirements for machine learning. This research is closely integrated with the existing educational activities, providing both undergraduate and graduate students with the opportunity to participate in cutting-edge science and technology in an innovative way. The investigators also provide educational outreach activities in integrated photonic devices, machine learning, and computer algorithms to promote the interests and participations of K-12 students and broaden the participations from underrepresented groups. Technical description: With funding from the Electrical, Communications and Cyber Systems (ECCS) Division, the investigators from the University of Pennsylvania and University of California, San Diego are developing a disruptive system-level integrated nanophotonic circuits – Programmable Photonic Computation Accelerators (PPCA) – through active control via strategic engineering of quantum symmetry, to perform real-time programmable mathematical operations and implement machine learning algorithms. Unique symmetry-driven geometries will be explored to deliver novel topological photonic components required for matrix multiplication, which can be dynamically programmed by flexible control of spatial-variant optical modulation. On the developed programmable photonic computation accelerator platform, different iconic machine learning algorithms will be performed to demonstrate optical machine learning for the first time and test its corresponding speed and fidelity. The investigators have highly complementary expertise on active photonic circuits, integrated devices, systems, and packaging, as well as computation and machine learning, which will be actively synergized, enabling a paradigmatic shift towards system-level integration of large-scale photonic computation accelerators. If successful, the innovative programmable photonic computation accelerators could be applied in the domains which demand extreme speed, energy efficiency, parallelism, significant complexity, and high scalability on an ultra-compact footprint, and full programmability.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.

随着第四次工业革命的临近，大规模计算变得比以往任何时候都更加需要和流行。然而，传统电子微处理器的性能几乎已经达到了设备速度、片上密度和功耗的极限，将无法继续维持即将到来的数据爆炸。与电子产品相比，光学计算速度非常快，功耗要求很低，因为它具有固有的高速、大带宽和无限并行性，这对于缓解与需要实时做出人工智能决策的应用程序相关的数据流量至关重要。现代人工智能的数据驱动训练需要新的可编程计算方法。在这个项目中，研究人员将利用最先进的集成光子学技术开发一种创新的可编程光子计算加速器（PPCA），加快计算速度，降低成本和能耗，以维持机器学习的长期性能要求。这项研究与现有的教育活动紧密结合，为本科生和研究生提供了以创新的方式参与前沿科学技术的机会。研究人员还提供集成光子设备，机器学习和计算机算法的教育外展活动，以促进K-12学生的兴趣和参与，并扩大代表性不足群体的参与。技术描述:在电子、通信和网络系统（ECCS）部门的资助下，宾夕法尼亚大学和加州大学圣地亚哥分校的研究人员正在开发一种颠覆性的系统级集成纳米光子电路——可编程光子计算加速器（PPCA）——通过量子对称战略工程的主动控制，执行实时可编程数学运算和实现机器学习算法。将探索独特的对称驱动几何结构，以提供矩阵乘法所需的新型拓扑光子元件，这些元件可以通过灵活控制空间可变光学调制来动态编程。在开发的可编程光子计算加速器平台上，将执行不同的标志性机器学习算法，首次演示光学机器学习，并测试其相应的速度和保真度。研究人员在有源光子电路，集成器件，系统和封装以及计算和机器学习方面具有高度互补的专业知识，这些专业知识将积极协同，使大规模光子计算加速器的系统级集成成为可能。如果成功，创新的可编程光子计算加速器可以应用于需要极高速度、能效、并行性、显著复杂性、超紧凑足迹和完全可编程性的高可扩展性的领域。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。

项目成果

Band-limited photodetection of temporal coherence

时间相干性的带限光电检测

• DOI: 10.1364/oe.462445
• 发表时间: 2023
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Chen, Zijun;Fainman, Yeshaiahu
• 通讯作者: Fainman, Yeshaiahu

Universal photonics tomography

通用光子断层扫描

• DOI: 10.1364/oe.454497
• 发表时间: 2022
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Gaur, Prabhav;Grieco, Andrew;Alshamrani, Naif;Almutairi, Dhaifallah;Fainman, Yeshaiahu
• 通讯作者: Fainman, Yeshaiahu

Optical bistability in PECVD silicon-rich nitride

PECVD 富硅氮化物的光学双稳定性

• DOI: 10.1364/oe.473928
• 发表时间: 2022
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Friedman, Alex;Belogolovskii, Dmitrii;Grieco, Andrew;Fainman, Yeshaiahu
• 通讯作者: Fainman, Yeshaiahu

SERS-based ssDNA composition analysis with inhomogeneous peak broadening and reservoir computing

• DOI: 10.1063/5.0075528
• 发表时间: 2022-01
• 期刊: Applied Physics Letters
• 影响因子: 4
• 作者: Phuong H. L. Nguyen;Shimon Rubin;P. Sarangi;Piya Pal;Y. Fainman

Extraction of Coupling Coefficients of Directional Couplers Using Resonance Splitting

• DOI: 10.1109/jphot.2022.3221487
• 发表时间: 2022-12
• 期刊: IEEE Photonics Journal
• 影响因子: 2.4
• 作者: Ang Li;Y. Fainman