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CAREER: Enabling Efficient AI Computing at Scale with Heterogeneous Retention-Aware Memory Systems

US NSF grant open #nsf-2541050

Summary

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 this behavior, as they are typically designed as one-size-fits-all storage, resulting in excessive ener

CAREER: Enabling Efficient AI Computing at…
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