Spatial Variation-Aware Read Disturbance Defenses: Experimental Analysis of Real DRAM Chips and Implications on Future Solutions

2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)(2024)

引用 0|浏览14
暂无评分
摘要
Read disturbance in modern DRAM chips is a widespread phenomenon and is reliably used for breaking memory isolation, a fundamental building block for building robust systems. RowHammer and RowPress are two examples of read disturbance in DRAM where repeatedly accessing (hammering) or keeping active (pressing) a memory location induces bitflips in other memory locations. Unfortunately, shrinking technology node size exacerbates read disturbance in DRAM chips over generations. As a result, existing defense mechanisms suffer from significant performance and energy overheads, limited effectiveness, or prohibitively high hardware complexity. In this paper, we tackle these shortcomings by leveraging the spatial variation in read disturbance across different memory locations in real DRAM chips. To do so, we 1) present the first rigorous real DRAM chip characterization study of spatial variation of read disturbance and 2) propose Svärd, a new mechanism that dynamically adapts the aggressiveness of existing solutions based on the row-level read disturbance profile. Our experimental characterization on 144 real DDR4 DRAM chips representing 10 chip designs demonstrates a large variation in read disturbance vulnerability across different memory locations: in the part of memory with the worst read disturbance vulnerability, 1) up to 2x the number of bitflips can occur and 2) bitflips can occur at an order of magnitude fewer accesses, compared to the memory locations with the least vulnerability to read disturbance. Svärd leverages this variation to reduce the overheads of five state-of-the-art read disturbance solutions, and thus significantly increases system performance.
更多
查看译文
关键词
DRAM,Memory,Security,Reliability,Read Disturbance,RowHammer,RowPress,Real Chip,Characterization,Spatial Variation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要