Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
This work proposes MECR, a lightweight market-driven page-retention model for hybrid DRAM–NVM memory systems. MECR integrates a contextual bandit learner with endurance-aware credit bidding to optimize hit rate, fairness, and NVM lifetime. A deep verification layer ensures safe eviction decisions under wear-sensitive workloads. Experimental evaluation using synthetic memory traces shows stable accuracy, improved fairness, and adaptive DRAM allocation.
Included in
GRP-1203 Market-Driven Endurance Credits for Page Retention (MECR) in Hybrid DRAM–NVM Systems
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
This work proposes MECR, a lightweight market-driven page-retention model for hybrid DRAM–NVM memory systems. MECR integrates a contextual bandit learner with endurance-aware credit bidding to optimize hit rate, fairness, and NVM lifetime. A deep verification layer ensures safe eviction decisions under wear-sensitive workloads. Experimental evaluation using synthetic memory traces shows stable accuracy, improved fairness, and adaptive DRAM allocation.