Project Grant 2304348
Award Date
9/1/23
Completion Date
5/31/24
Dollars Obligated
$274K
Federal Agency
National Science Foundation
Federal Grant Program
47.084
Assistance Type
Project Grant
Place of Performance
Framingham, MA 01701, USA
Similar Awards
Semiotic AI, Inc. received a $256,000 Small Business Innovation Research Phase I Project Grant from the National Science Foundation to accelerate machine learning on encrypted data from July 1, 2021 to March 31, 2022. Funded through the NSF's Engineering program (CFDA #47.041), which seeks to improve quality of life and economic strength through engineering innovation and excellence, the project aims to develop techniques that enable machine learning to be performed directly on encrypted data...
This $209,368 federal Project Grant award from the National Science Foundation's Integrative Activities program (CFDA 47.083) aims to develop trusted, low-overhead tools that enable computation directly on encrypted data. The goal is to accelerate the creation of new capabilities that allow confidential data to be shared with untrusted parties who can extract insights without accessing the unencrypted data. This would increase public trust in modern AI tools and enable data-powered, socially...
This $240,062 project grant, awarded by the National Science Foundation (NSF) through its Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), aims to develop efficient and scalable hardware architectures for privacy-preserving neural network inference based on ciphertext-ciphertext fully homomorphic encryption (FHE). The research will focus on designing optimized hardware building blocks, such as polynomial multipliers, and a reconfigurable FHE architecture that...
This $174,810 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports research at Northern Arizona University (NAU) to optimize resource-intensive operations in existing fully homomorphic encryption schemes and integrate the optimized algorithm into federated learning frameworks. The project aims to enhance security in Internet of Things communications by enabling secure data analysis on encrypted data...
This $149,156 project grant, awarded on October 1, 2023 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, will fund the development of custom hardware accelerators for privacy-preserving image processing using homomorphic encryption. The key products to be delivered include: Hardware accelerators for homomorphic encryption-based noise cancellation and contrast enhancement of images, leveraging numerical approaches for division and min/max...
This federal Project Grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to design and develop a secure and efficient decentralized federated learning (DFL) system. The $380,667 grant, awarded on August 15, 2024, will fund research to address communication, computation, and security issues in DFL, which enables training of data-hungry machine learning models on local devices without sharing raw data. The...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $291,703 to the Regents of the University of Minnesota to accelerate the development of practical and scalable privacy-preserving machine learning (ML) solutions. The project aims to create efficient, crypto-friendly neural architectures and algorithm-hardware co-design methodologies to significantly speed up privacy-preserving ML on hardware platforms like...
This $219,332 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program aims to develop a secure and efficient decentralized federated learning (DFL) system. The key products and services to be delivered include: Developing computational theories, models, and prototype systems to establish the foundations for trustworthy DFL, addressing both high-performance accuracy and security with privacy...
The National Science Foundation (NSF) awarded a $581,966 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Pittsburgh. The grant, awarded on May 1, 2024, with a completion date of April 30, 2027, supports the development of a hardware-software co-design framework to address performance and memory space issues for privacy protection in cloud-based deep learning recommendation systems (DLRMs). Key project objectives include: 1)...