Project Grant 2606338
- This $424,750 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) supports the development of an ultrashort-pulse optical neural network system for brain-scale computing. The research aims to leverage the speed, bandwidth, and low-loss properties of light to enable real-time processing of billion-scale neural network models using minimal spatial elements. This approach seeks to overcome the limitations of current optical computing architectures. The...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Rochester Institute of Technology $276,479 on September 1, 2026, under the Engineering program (CFDA 47.041) to advance distributed optimization theory and algorithms that reduce communication overhead in collaborative artificial intelligence systems. The project develops methods enabling participating devices to exchange only single numerical values during distributed learning, making...
- The University of Rochester received a $1,499,921 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems through the Engineering program (CFDA 47.041) to develop a novel photonic non-Von Neumann computing system using optical frequency combs. The four-year project aims to push the boundaries of nature-based computing in efficiency, capability and applicability for machine learning algorithms. Key products and services include...
- This $375,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to accelerate the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, through a data-driven deep learning approach. The project, led by the Georgia Tech Research Corporation, will establish deep learning frameworks to construct photonic metamaterials, integrate information on tailorable optical...
- The National Science Foundation (NSF) awarded a $600,000 project grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Regents of the University of Michigan to support research on reinforcement learning and transformer-inspired approaches for smart photonics inverse design. The project aims to enable non-experts to use artificial intelligence models to design sophisticated photonic structures for optical applications, while also advancing...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Rochester Institute of Technology $514,145 on May 1, 2026, under the NSF Engineering program (CFDA 47.041) to develop ionically gated transistors that combine memory and processing within a single material for energy-efficient artificial intelligence on edge devices. The research establishes the fundamental science and engineering of a dual-mode ionically gated transistor functioning as a...
- The National Science Foundation awarded Photopica LLC $305,000 on July 15, 2026, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop an AI-accelerated simulation platform for high-power fiber lasers. The recipient will build a machine-learning-enhanced modeling system that simulates nonlinear optical effects in high-power fiber lasers significantly faster than current methods. The core technical approach combines a phase-matched model of transverse mode...
- This National Science Foundation (NSF) project grant, awarded under the Engineering (CFDA 47.041) program, supports the development of new optical devices called "intersubband neurons" that could enable ultrafast optical neural networks. The $352,180 award to the University of Texas at Austin, running from October 1, 2023 to March 31, 2026, aims to create these novel photonic devices that can perform computations at the speed of light, potentially outpacing electronic neural...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $750,000 to Northeastern University to develop innovative engineered photonic materials using a data-driven deep learning approach. The research aims to accelerate the discovery, design, and implementation of new photonic metamaterials with tailored optical properties for applications in areas such as lasers, optical communications, quantum computing, and...
- This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) project grant award provides $650,000 to The Trustees of Columbia University in the City of New York to develop energy-efficient optical brain-inspired (neuromorphic) computing devices. The project aims to establish a 3D nanofabrication platform that combines DNA-programmable assembly and conventional lithographic methods to create novel optical metamaterials and integrate them into neuromorphic...
The National Science Foundation awarded Rochester Institute of Technology $154,723 on October 1, 2025, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop a scalable deep learning architecture compatible with photonic and optoelectronic technologies for real-time optical signal processing. The project addresses the challenge that conventional electronic computing faces meeting the processing demands of artificial intelligence and machine learning, while photonic neural networks currently remain impractical due to scalability constraints and reliance on electronic circuits for nonlinear effects, negating photonics' speed advantages. RIT will design a novel deep learning architecture that significantly reduces optical neural network size and enables photonic-based signal processing for telecommunications, imaging, biomedical applications, and quantum information processing systems. The work extends through October 31, 2026, and is performed in Rochester, New York. Two sub-recipients deliver specialized technical contributions: The University of Central Florida Board of Trustees (sub-award dated May 6, 2026) will develop theoretical and experimental foundations for an integrated photonic deep learning platform and research fundamental challenges of advanced semiconductor integrated photonic circuits for unconventional computing. Georgia TECH Research Corp (sub-award dated July 6, 2026) will develop a miniaturized reconfigurable photonic platform through integration of CMOS-compatible substrates, including silicon nitride and silicon, with phase-change materials, demonstrating phase shifters at least two orders of magnitude smaller than conventional thermo-optic or free-carrier plasma-dispersion approaches for a given phase shift.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $154.7k | 4/6/26 |
GrantNumber | Description | Subgrantee | Prime Award | Dollars Obligated | Updated At |
|---|---|---|---|---|---|
2322202S | Georgia TECH Research Corp | Project Grant 2606338 | $350.4k | 8/24/26 | |
2322201S | The University Of Central Florida Board Of Trustees | Project Grant 2606338 | $437.5k | 6/25/26 |