ASCENT: Collaborative Research: Programmable Photonic Computation Accelerators (PPCA)
ASCENT: Collaborative Research: Programmable Photonic Computation Accelerators (PPCA)
批准号:
2023780
负责人:
Liang Feng
金额:
$85.0万
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
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31
中文摘要
随着第四次工业革命的临近,大规模计算的要求和普及程度比以往任何时候都要高。然而,传统电子微处理器的性能几乎已经达到了器件速度、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。与电子计算相比,光学计算可以非常快且具有低功耗要求,因为其固有的高速、大带宽和无限并行对于缓解与需要实时做出人工智能决策的应用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,降低成本和能源消耗,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技的机会。调查人员还在集成光子设备、机器学习和计算机算法方面提供教育推广活动,以促进K-12学生的兴趣和参与,并扩大代表不足的群体的参与。技术描述:在电气、通信和网络系统(ECCS)部门的资助下,来自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破坏性的系统级集成纳米光子电路-可编程光子计算加速器(PPCA)-通过通过量子对称战略工程的主动控制来执行实时可编程数学运算和实施机器学习算法。将探索独特的对称性驱动几何结构,以提供矩阵乘法所需的新型拓扑光子组件,该组件可以通过灵活控制空间变化的光学调制进行动态编程。在开发的可编程光子计算加速器平台上,将首次执行不同的标志性机器学习算法来演示光学机器学习,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性的专业知识,这些专业知识将积极协同,使大规模光子计算加速器向系统级集成的典范转变。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、显著的复杂性和超紧凑占地面积的高可扩展性以及完全可编程性的领域。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
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.
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DOI:
10.1145/3519595
发表时间:
2022-02
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
作者:
[Yadi Cao;Yunuo Chen;Minchen Li;Yin Yang;Xinxin Zhang;Mridul Aanjaneya;Chenfanfu Jiang]
通讯作者:
Yadi Cao;Yunuo Chen;Minchen Li;Yin Yang;Xinxin Zhang;Mridul Aanjaneya;Chenfanfu Jiang
DOI:
10.1002/nme.6668
发表时间:
2020-03
期刊:
International Journal for Numerical Methods in Engineering
影响因子:
2.9
作者:
[Yue Li;Xuan Li;Minchen Li;Yixin Zhu;Bo Zhu;Chenfanfu Jiang]
通讯作者:
Yue Li;Xuan Li;Minchen Li;Yixin Zhu;Bo Zhu;Chenfanfu Jiang
DOI:
10.1145/3592104
发表时间:
2023-07
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
作者:
[L. Lan;Minchen Li;Chenfanfu Jiang;Huamin Wang;Yin Yang]
通讯作者:
L. Lan;Minchen Li;Chenfanfu Jiang;Huamin Wang;Yin Yang
DOI:
10.1145/3414685.3417863
发表时间:
2020-11
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
作者:
[Tao Xue;Haozhe Su;Chengguizi Han;Chenfanfu Jiang;Mridul Aanjaneya]
通讯作者:
Tao Xue;Haozhe Su;Chengguizi Han;Chenfanfu Jiang;Mridul Aanjaneya
A unified second-order accurate in time MPM formulation for simulating viscoelastic liquids with phase change
用于模拟相变粘弹性液体的统一二阶实时精确 MPM 公式
DOI:
10.1145/3450626.3459820
发表时间:
2021
期刊:
ACM Transactions on Graphics
影响因子:
6.2
作者:
[Su, Haozhe, Xue, Tao, Han, Chengguizi, Jiang, Chenfanfu, Aanjaneya, Mridul]
通讯作者:
Aanjaneya, Mridul
共 21 条
Collaborative Research: First-Principle Control of Novel Resonances in Non-Hermitian Photonic Media
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批准号:2326699
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项目类别:Standard Grant
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资助金额:$41.61万
-
财政年份:2023
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负责人:Liang Feng
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依托单位:
MRI: Acquisition of an Electron-Beam Lithography Tool for Research, Education and Training
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批准号:2117775
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项目类别:Standard Grant
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资助金额:$120.0万
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财政年份:2021
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负责人:Liang Feng
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依托单位:
CAREER: Topological Engineering for Active Photonic Structures and Devices
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批准号:1846766
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项目类别:Continuing Grant
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资助金额:$50.0万
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财政年份:2019
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负责人:Liang Feng
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依托单位:
New Microlasers: Structuring and Twisting Laser Radiations at a Microscale
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批准号:1932803
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项目类别:Standard Grant
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资助金额:$37.0万
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财政年份:2019
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负责人:Liang Feng
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依托单位:
RAISE-EQuIP: Integrated Higher-Dimensional Quantum Photonic Platform
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批准号:1842612
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项目类别:Standard Grant
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资助金额:$75.0万
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财政年份:2018
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负责人:Liang Feng
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依托单位:
High spatial resolution tactile sensing imager using optical exceptional point structures
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批准号:1811393
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项目类别:Standard Grant
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资助金额:$12.92万
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财政年份:2017
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负责人:Liang Feng
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依托单位:
Collaborative Research: Investigation of Rotation-Time and Inversion-Time Symmetries in Photonic Materials
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批准号:1811370
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项目类别:Continuing Grant
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资助金额:$10.08万
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财政年份:2017
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负责人:Liang Feng
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依托单位:
Laser Chip Lithography-Patterned Nanomembranes for Wastewater Treatment
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批准号:1635026
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项目类别:Standard Grant
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资助金额:$25.0万
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财政年份:2016
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负责人:Liang Feng
-
依托单位:
Collaborative Research: Investigation of Rotation-Time and Inversion-Time Symmetries in Photonic Materials
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批准号:1506884
-
项目类别:Continuing Grant
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资助金额:$21.0万
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财政年份:2015
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负责人:Liang Feng
-
依托单位:
High spatial resolution tactile sensing imager using optical exceptional point structures
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批准号:1507312
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项目类别:Standard Grant
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资助金额:$34.96万
-
财政年份:2015
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负责人:Liang Feng
-
依托单位:
海外基金