CAREER: Foundations of Memory-Constrained Machine Learning — NSF Award to Rutgers University New Brunswick (NJ, $344,821)
Memory required to perform a computational task is one of the most fundamental measures used by theoretical computer scientists to assess how difficult a task is. Nevertheless, in practice, memory optimization received limited attention until the emergence of big data applications. More recently, the rapid growth of la
| Award title | CAREER: Foundations of Memory-Constrained Machine Learning |
|---|---|
| Award ID | 2542741 |
| Awardee | Rutgers University New Brunswick |
| City | NEW BRUNSWICK |
| State | NJ |
| Amount obligated | $344,821 |
| Principal investigator | Sumegha Garg |
| Program | Algorithmic Foundations |
| Start date | 06/01/2026 |
| Abstract | Memory required to perform a computational task is one of the most fundamental measures used by theoretical computer scientists to assess how difficult a task is. Nevertheless, in practice, memory optimization received limited attention until the emergence of big data applications. More recently, the rapid growth of large-scale machine learning (ML) systems, including large language models (LLMs), has pushed model parameter counts far beyond improvements in memory hardware, raising concerns that |
| Source | NSF Awards |
$799/mo
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