Selective Subarray Isolation for Mitigating RowHammer Attack

RowHammer is a severe circuit-level vulnerability in DRAM-based main memories that allows attackers to flip the bits stored in DRAM rows by repeatedly accessing the nearby rows. Due to density scaling, newer generation DRAM chips are found to be increasingly more vulnerable to RowHammer attacks, motivating researchers from both academia and industry to come up with new RowHammer attack patterns and mitigation strategies that can be widely adopted. However, the question remains whether the mitigation strategies available now can secure DRAM-based memory in the future. We propose three approaches to mitigate RowHammer attacks by exploiting subarray isolation. A subarray is a collection of DRAM rows in a DRAM bank where each subarray operates independently. In the first approach, known as Subarray Isolation (SI), data from different domains are allocated to separate subarrays in DRAM. The SI strategy naively allocates subarrays to domains, greatly hampering the bank-level parallelism in memory accesses, leading to a significant performance loss. The second approach, namely, Selective Subarray Isolation (SSI), improves this aspect. With the SSI strategy, we allocate only confidential data from different domains to separate subarrays. The non-confidential data of the domains will share the subarrays as in the conventional case. Our evaluations show that the SSI strategy performs better compared to state-of-the-art mitigation strategies when the amount of confidential data is less. To further improve performance, we propose the third approach, namely Finer Selective Subarray Isolation (FSSI), which allocates separate partitions protected with guard rows within a subarray to confidential data from different domains. Our evaluations show that, of the three approaches, the FSSI strategy performs the best. Compared to baseline without any RowHammer protection, the FSSI strategy experiences an average performance drop of 0.89% for 50% of confidential data, but for 10% and 20% of confidential data, it shows an improvement of 1.43% and 1.28%, respectively. We also observe that the FSSI strategy is the most energy efficient among the state-of-the-art RowHammer mitigation techniques. Note that all our proposed strategies do not incur hardware overhead for performing RowHammer mitigation.