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PRACtical: Subarray-Level Counter Update and Bank-Level Recovery Isolation for Efficient PRAC Rowhammer Mitigation

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arxiv 2507.18581 v1 pith:VAKGB76W submitted 2025-07-24 cs.AR cs.ET

classification cs.ARcs.ET
keywords performancepraccountermitigationpracticalrowhammeractivationsbank-level
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

As DRAM density increases, Rowhammer becomes more severe due to heightened charge leakage, reducing the number of activations needed to induce bit flips. The DDR5 standard addresses this threat with in-DRAM per-row activation counters (PRAC) and the Alert Back-Off (ABO) signal to trigger mitigation. However, PRAC adds performance overhead by incrementing counters during the precharge phase, and recovery refreshes stalls the entire memory channel, even if only one bank is under attack. We propose PRACtical, a performance-optimized approach to PRAC+ABO that maintains the same security guarantees. First, we reduce counter update latency by introducing a centralized increment circuit, enabling overlap between counter updates and subsequent row activations in other subarrays. Second, we enhance the $RFM_{ab}$ mitigation by enabling bank-level granularity: instead of stalling the entire channel, only affected banks are paused. This is achieved through a DRAM-resident register that identifies attacked banks. PRACtical improves performance by 8% on average (up to 20%) over the state-of-the-art, reduces energy by 19%, and limits performance degradation from aggressive performance attacks to less than 6%, all while preserving Rowhammer protection.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ShadowScope: GPU Monitoring and Validation via Composable Side Channel Signals

    cs.CR 2025-08 conditional novelty 5.0 of 10

    ShadowScope detects GPU kernel attacks by segmenting kernel execution with marker functions and comparing PMU traces against pre-collected golden references, achieving up to 100% detection in its experiments.

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