This $300,000 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a new, efficient, and cost-effective testing methodology for advanced integrated circuits. The research project, led by the Georgia Tech Research Corporation, will generate compact test patterns that can effectively detect internal cell-level defects in modern ICs without relying on exhaustive circuit simulations. This approach...
This $430,234 National Science Foundation project grant supports the development of testing and design-for-test techniques for monolithic 3D integrated circuits at Arizona State University. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the award will advance research on built-in self-test solutions, test generation methods, and power distribution designs to enable reliable testing and defect isolation in multitiered 3D chip architectures. Outcomes...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enable developers to perform fine-grained software testing, thereby increasing software quality. The research project will: (1) develop a language and framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests; (3) adapt fine-grained tests to software evolution and...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enable developers to perform fine-grained software testing, thereby increasing software quality. The key objectives are to: (1) develop a language and framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests; (3) adapt fine-grained tests to software evolution and...
This $500,000 National Science Foundation project grant supports research at the University of California, Riverside to develop novel machine learning-based electromigration analysis and optimization methods for very large-scale integrated circuit design. Specifically, the university will explore enhanced physics-informed neural network approaches for multi-segment interconnect stress analysis and full-chip electromigration-induced voltage drop modeling. Researchers will also develop efficient...
This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant award of $275,000 to Grayskytech, Inc. aims to develop a novel technology that will dramatically improve the speed and cost-effectiveness of integrated circuit (IC) design verification. The innovation involves a highly parallel, configurable computer architecture that can effectively turn a single FPGA into hundreds of fast virtual processors. This will enable 30x faster behavioral...
This National Science Foundation (NSF) CAREER grant of $123,451, awarded under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to improve the reliability and efficiency of software regression testing in the presence of flaky tests. The project aims to: (1) reduce the cost of flaky-test detection and debugging techniques; (2) develop new techniques to predict flakiness-related properties; (3) speed up and reduce resources needed for...
This Project Grant from the National Science Foundation supports research at the University of Southern California to develop a principled framework for modeling and analyzing the impact of hardware faults on embedded software. The award of $374,999 under the Computer and Information Science and Engineering program will fund research over four years from July 2022 to June 2026. The researchers will design an instruction-set architecture-level fault model to capture the effects of hardware...
This $250,000 Project Grant, awarded by the National Science Foundation's Computer and Information Science and Engineering (CISE) program, supports a collaborative research effort focused on developing formal methods to synthesize and verify in-memory computing systems for neural networks. The project aims to: Verify the reliability of analog and digital in-memory computing (IMC) circuits used to accelerate neural networks, and Leverage machine learning and formal methods to synthesize...
This $200,000 Project Grant award, funded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports a collaborative research project to develop new diagnostic testing methods for the Radio Access Network (RAN) subsystem of 5G and future mobile network technologies. The project aims to enhance RAN testing in three key areas: (1) improving test coverage by identifying dependencies and root causes, (2) enabling non-intrusive,...