This $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Northeastern University to improve the security of machine learning models in multi-tenant cloud field programmable gate array (FPGA) environments. The three-year award aims to advance understanding of vulnerabilities in cloud-FPGAs shared by multiple tenants, where a malicious actor could potentially manipulate another tenant's machine learning...
This National Science Foundation award provides $498,003 to Arizona State University under the Computer and Information Science and Engineering program to develop security countermeasures for field-programmable gate array (FPGA)-as-a-service systems. The university will conduct research on authentication methods, information flow tracking, formal methods, and machine learning to detect malicious FPGA bitstreams. Evaluation will utilize Xilinx Kintex-7 and Zynq-7000 FPGAs to develop benchmarks...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) project grant award, with CFDA Number 47.070, supports research to study and mitigate "FPGA pentimenti" - the unintended data leakage between subsequent users of cloud-based Field Programmable Gate Array (FPGA) hardware accelerators. The $189,509 award to the University of Washington aims to characterize the analog side-channel effects leading to this data leakage, establish bounds on the...
The National Science Foundation (NSF) awarded a $299,995 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Massachusetts (UMass) to develop novel security approaches for protecting field-programmable gate arrays (FPGAs) used in cloud computing environments. The key objectives of this 3-year project are to introduce secure operating mechanisms and a security and queue management unit (SQMU) to enable controlled sharing of FPGAs...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
The National Science Foundation awarded a $100,000 Project Grant to the University of California, Merced under the NSF Technology, Innovation, and Partnerships federal grant program (CFDA 47.084) to develop artificial intelligence models for financial fraud detection. Specifically, the university will use tree-based machine learning techniques to create fraud detection software with predictive accuracy exceeding industry standards. The software aims to save banks millions annually by reducing...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) project grant (CFDA 47.070) awarded $160,493 to the Georgia Tech Research Corporation to conduct collaborative research at the intersection of distributed cryptography and blockchain technologies. The key objectives are: (1) developing a common framework and language to bridge the gap between cryptography and distributed computing research on emerging technologies like blockchain; (2) investigating the...
This Project Grant award of $644,035 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to enhance the performance and programmability of field programmable gate arrays (FPGAs) through the integration of compute-in-memory (CIM) technology. The key products and services delivered through this award include: Developing architectural enhancements to enable CIM support within FPGA memory blocks,...
This $500,000 National Science Foundation project grant supports the development of new cyberattack detection approaches for large networks using complex graph modeling. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), key products include constructing a complex graph model with graph refinement techniques to represent network traffic. The University of Virginia will design semi-supervised and weakly-supervised graph-based learning algorithms leveraging...
The Georgia State University Research Foundation Inc. was awarded a two-year, $255,245 Project Grant from the National Science Foundation Division of Information and Intelligent Systems to develop green granular neural networks with fast, FPGA-based incremental transfer learning algorithms. Under the Computer and Information Science and Engineering federal grant program, the awardee will create a novel shallow software-hardware machine learning system using green and energy efficient field...
This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $554,699 to Georgia Tech Research Corporation (Georgia Tech) to develop a distributed FPGA (field-programmable gate array) system for real-time fraud detection on large-scale dynamic financial activity graphs. The project aims to achieve microsecond-level latency in detecting fraudulent transactions using graph neural networks (GNNs), which is a significant advancement over current systems. The key tasks include constructing the scalable FPGA system, optimizing its performance as the graphs evolve dynamically, and modeling system uncertainty to guide decision-making. Successful implementation of this system could enhance fraud detection capabilities for millions of consumers globally and help businesses reduce fraud losses. The project outcomes are expected to have applications in domains such as cybercrime detection, insurance fraud, and national security. All project code will be open-sourced to benefit the broader community.