We introduce ABACuS, a new low-cost hardware-counter-based RowHammer mitigation technique that performance-, energy-, and area-efficiently scales with worsening RowHammer vulnerability. We observe that both benign workloads and RowHammer attacks tend to access DRAM rows with the same row address in multiple DRAM banks at around the same time. Based on this observation, ABACuS's key idea is to use a single shared row activation counter to track activations to the rows with the same row address in all DRAM banks. Unlike state-of-the-art RowHammer mitigation mechanisms that implement a separate row activation counter for each DRAM bank, ABACuS implements fewer counters (e.g., only one) to track an equal number of aggressor rows. Our evaluations show that ABACuS securely prevents RowHammer bitflips at low performance/energy overhead and low area cost. We compare ABACuS to four state-of-the-art mitigation mechanisms. At a near-future RowHammer threshold of 1000, ABACuS incurs only 0.58% (0.77%) performance and 1.66% (2.12%) DRAM energy overheads, averaged across 62 single-core (8-core) workloads, requiring only 9.47 KiB of storage per DRAM rank. At the RowHammer threshold of 1000, the best prior low-area-cost mitigation mechanism incurs 1.80% higher average performance overhead than ABACuS, while ABACuS requires 2.50X smaller chip area to implement. At a future RowHammer threshold of 125, ABACuS performs very similarly to (within 0.38% of the performance of) the best prior performance- and energy-efficient RowHammer mitigation mechanism while requiring 22.72X smaller chip area. ABACuS is freely and openly available at https://github.com/CMU-SAFARI/ABACuS.

翻译：我们提出ABACuS——一种新型低成本基于硬件计数器的RowHammer缓解技术，能够在性能、能耗和面积效率上随RowHammer脆弱性恶化而扩展。我们观察到，良性工作负载与RowHammer攻击在多个DRAM存储体中访问相同行地址的DRAM行时，往往具有时间同步性。基于此发现，ABACuS的核心思想是：使用单一共享行激活计数器，追踪所有DRAM存储体中具有相同行地址的行的激活次数。与当前最优的RowHammer缓解机制（为每个DRAM存储体配置独立行激活计数器）不同，ABACuS通过部署更少的计数器（例如仅一个）来追踪等量的攻击者行。评估表明，ABACuS能够以较低的性能/能耗开销和面积成本安全预防RowHammer位翻转。我们将ABACuS与四种最先进的缓解机制对比：在近未来RowHammer阈值为1000时，ABACuS在62个单核（八核）工作负载上的平均性能开销仅为0.58%（0.77%），DRAM能耗开销为1.66%（2.12%），每DRAM rank仅需9.47 KiB存储空间；在RowHammer阈值为1000时，现有最优低面积成本缓解机制的平均性能开销比ABACuS高1.80%，而ABACuS实现所需芯片面积仅为前者的1/2.50倍。在未来的RowHammer阈值125场景下，ABACuS的性能表现与当前最优性能/能耗平衡的RowHammer缓解机制极为接近（性能差异在0.38%以内），但所需芯片面积仅为后者的1/22.72倍。ABACuS已在https://github.com/CMU-SAFARI/ABACuS免费开源。