star-laboratory/prismPrISM: A Scalable Probabilistic RowHammer Mitigation (ISCA 2026). Ramulator2 source code and evaluation scripts.
To appear in the 53rd International Symposium on Computer Architecture (ISCA), 2026.
Jeonghyun Woo1*, Junsu Kim1*, Aamer Jaleel2, Prashant J. Nair1
1 University of British Columbia 2 NVIDIA * Equal contribution
DRAM scaling has exacerbated the RowHammer vulnerability. To counter this, JEDEC recently introduced Per Row Activation Counting (PRAC) with the Alert Back-Off protocol as an optional DDR5 feature. While promising, PRAC requires per-row counter cells that incur area overhead, and updating them on every activation lengthens DRAM timing parameters, degrading performance. Probabilistic mitigations such as MINT offer a lower-cost alternative by randomly selecting and mitigating rows within periodic mitigation windows. MINT is effective at higher thresholds (≥ 1000), but at lower thresholds, it must raise its mitigation rate to overcome the non-selection problem, where heavily hammered rows can repeatedly escape sampling. This fixed-rate scaling reduces effective memory bandwidth even when no attack is present.
To overcome this limitation, we propose PrISM, an intersection-based probabilistic mitigation that correlates sampled rows across windows using a Sampled History Queue (SHQ). PrISM samples a few activation slots per window, stores sampled-but-unmitigated rows in the SHQ, and requests an additional mitigation through the existing Alert Back-Off protocol when a sampled row reappears in this history. This allows PrISM to increase mitigation only when persistent row activity is observed, without globally increasing the fixed mitigation rate. At the threshold of 500, PrISM incurs a negligible 0.2% average slowdown compared to 14% for PRAC, with no DRAM array changes or per-row counters and only 625B of SRAM per bank — one to two orders of magnitude less than prior secure counter-based in-DRAM defenses. Compared to MINT, PrISM provides better scalability at low thresholds, reducing average slowdown from 10.7% to 1.5% at a threshold of 250, a 7.1× reduction.
prism/
├── perf_analysis/ # Ramulator2 simulations (Figures 7, 8, 9; Table VI)
├── security_analysis/ # Analytical model + MC validation (Figures 5, 6)
├── LICENSE
└── README.md # This file
Each subdirectory has its own README with detailed reproduction instructions:
perf_analysis/ — Ramulator2 infrastructure
for the performance results. Supports both SLURM and single-machine
execution.security_analysis/ — Pure-Python
analytical model and Monte-Carlo validation for the security claims.Clone the repository and pick a sub-directory:
git clone https://github.com/STAR-Laboratory/prism.git
cd prism
To reproduce the performance results (Figures 7, 8, 9; Table VI):
cd perf_analysis
./run_artifact.sh --method slurm --artifact all # or --method personal
To reproduce the security analysis (Figures 5, 6):
cd security_analysis
python3 prism_circular_security_analysis.py figure8 --w 72 \
--output-dir ./results --mc-windows 1000000 --mc-seeds 5
python3 plot_scripts/plot_figure5.py
See the sub-directory READMEs for full details.
@inproceedings{prism_isca2026,
title = {Loaded Dice: Solving the Non-Selection Problem for Scalable Probabilistic RowHammer Defense},
author = {Jeonghyun Woo and Junsu Kim and Aamer Jaleel and Prashant J. Nair},
booktitle = {53rd Annual International Symposium on Computer Architecture (ISCA)},
year = {2026},
series = {ISCA'26},
}
This repository is released under the MIT License.