This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $295,169 to Carnegie Mellon University (CMU) to develop an experimental approach that leverages large language models (LLMs) and rule-based symbolic AI to generate and verify high-performance math kernels. The project aims to address the challenge of ensuring the correctness of code snippets produced by generative AI systems, which lack the traditional human curation that previously provided some level of assurance. As part of the project, CMU will work with a subrecipient, Drexel University, to provide formal verification of the rules used in the lifting process, explore the use of testing and code analysis to assist in specification determination, and investigate bug analysis. These activities will support CMU's efforts in lifting and determining the specification and derivation of the algorithms implemented in the code generated by ChatGPT and similar LLM systems. The award period runs from October 1, 2024, to September 30, 2025.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $295.2k | 7/22/24 |
Grant Number | Description | Subgrantee | Prime Award | Dollars Obligated (Click to sort descending) | Updated At (Click to sort ascending) |
|---|---|---|---|---|---|
1127353488928S | Drexel University | Project Grant 2431265 | $113.6k | 10/4/24 |