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This paper introduces Rowhammer Vulnerability Count (RVC), a novel Rowhammer mitigation framework that directly estimates a row's vulnerability to bit flips instead of tracking activation counts. RVC selectively refreshes rows nearing bit flips, significantly reducing unnecessary refresh operations compared to prior methods like Graphene, Twice, and Hydra. Evaluations demonstrate that RVC achieves 95-99.99% reduction in mitigation-induced refreshes with no additional space overhead, while also improving energy efficiency and reducing average LLC latency by up to 76.91%.
Forget activation counts – RVC slashes Rowhammer mitigation overhead by up to 99.99% by directly tracking a row's vulnerability to bit flips.
The Rowhammer vulnerability poses an increasing challenge with newer generations of DRAM and aggressive technology scaling. Existing mitigation techniques, such as Graphene, Twice, and Hydra, primarily rely on tracking activation counts for each row and issuing refreshes when a row reaches a predefined tracking threshold. However, these methods have inherent limitations, including inefficiencies in identifying rows genuinely at risk of bit flips. In this paper, we propose a novel framework called Rowhammer Vulnerability Count (RVC), which shifts the focus from activation count tracking to evaluating a row's actual vulnerability to bit flips. By selectively issuing refreshes only to rows on the verge of experiencing bit flips, RVC drastically reduces unnecessary refresh operations. We also demonstrate that prior works have incorrectly set tracking thresholds, leading to security flaws. Our evaluation shows that RVC achieves 95 - 99.99% improvement in mitigation induced refreshes when compared to Graphene, with no additional space overhead. Furthermore, RVC improves energy efficiency and reduces average LLC latency by up to 76.91%, making it a highly efficient and scalable solution for addressing Rowhammer in modern DRAM systems. These findings establish RVC as a superior approach for preventing Rowhammer, outperforming existing methods in both accuracy and efficiency.
