CAREER: Enabling Efficient AI Computing at Scale with Heterogeneous Retention-Aware Memory — NSF Award to Stanford University (CA,
Modern artificial intelligence (AI) systems are increasingly limited not by arithmetic, but by memory. As frontier AI models become more capable, they require far more data to be moved, stored, and accessed efficiently. These workloads systematically generate large volumes of short-lived data that are written in memory
| Award title | CAREER: Enabling Efficient AI Computing at Scale with Heterogeneous Retention-Aware Memory |
|---|---|
| Award ID | 2541050 |
| Awardee | Stanford University |
| City | STANFORD |
| State | CA |
| Amount obligated | $424,237 |
| Principal investigator | Thierry Tambe |
| Program | Software & Hardware Foundation |
| Start date | 09/01/2026 |
| Abstract | Modern artificial intelligence (AI) systems are increasingly limited not by arithmetic, but by memory. As frontier AI models become more capable, they require far more data to be moved, stored, and accessed efficiently. These workloads systematically generate large volumes of short-lived data that are written in memory, consumed, and quickly discarded, as well as long-lived data that must be retained reliably across much longer time scales. Conventional memory systems are poorly optimized to thi |
| Source | NSF Awards |
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