AutoPRAC is the first automated model-checking framework for discovering attacks on PRAC Rowhammer mitigations, applied to reveal a flaw in MOAT.
Columndisturb: Understanding column-based read disturbance in real dram chips and implications for future systems
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
PVAC mitigates RowHammer by incrementing counters on victim rows and resetting them on activation via a parallel counter subarray, avoiding PRAC's saturation and timing penalties while supporting higher hammering tolerance.
A new OT protocol using quadratic residuosity that shifts computation to the sender, achieving 39.90 μs online receiver cost on IoT devices for 128-bit security, over 10x faster than SimplestOT.
PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
citing papers explorer
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AutoPRAC: Automating Attack Discovery for PRAC-Based Rowhammer Defenses using Model Checkers
AutoPRAC is the first automated model-checking framework for discovering attacks on PRAC Rowhammer mitigations, applied to reveal a flaw in MOAT.
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PVAC: A RowHammer Mitigation Architecture Exploiting Per-victim-row Counting
PVAC mitigates RowHammer by incrementing counters on victim rows and resetting them on activation via a parallel counter subarray, avoiding PRAC's saturation and timing penalties while supporting higher hammering tolerance.
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I-(OT)^2: A Client-optimal Oblivious Transfer Protocol for IoT Devices
A new OT protocol using quadratic residuosity that shifts computation to the sender, achieving 39.90 μs online receiver cost on IoT devices for 128-bit security, over 10x faster than SimplestOT.
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Loaded Dice: Solving the Non-Selection Problem for Scalable Probabilistic RowHammer Defense
PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
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ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.