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| author | Ataberk Olgun <olgunataberk@users.noreply.github.com> | 2022-12-02 16:15:22 +0300 |
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| committer | GitHub <noreply@github.com> | 2022-12-02 16:15:22 +0300 |
| commit | 5ddc00b3dabc8b59347ad2dcaafe22273bdef976 (patch) | |
| tree | 259ddd51ec8e1a7a2418405436c5b91056b42c82 | |
| parent | a75c3d3353791445855b3489265255c62142ca3e (diff) | |
| download | dram-bender-5ddc00b3dabc8b59347ad2dcaafe22273bdef976.tar.gz | |
Update README.md
| -rw-r--r-- | README.md | 8 |
1 files changed, 8 insertions, 0 deletions
@@ -33,6 +33,14 @@ U-TRR's sources are published as a separate repository and can be found [at this QUAC-TRNG's sources are published both as a separate repository [at this link](https://github.com/CMU-SAFARI/QUAC-TRNG), and under `sources/apps/QUAC-TRNG` directory inside this repository. +We plan to open source the following prior research works that used DRAM Bender as part of future work: + +- Revisiting RowHammer: An Experimental Analysis of Modern DRAM Devices and Mitigation Techniques, [link to arXiv](https://arxiv.org/abs/2005.13121) +- A Deeper Look into RowHammer's Sensitivities: Experimental Analysis of Real DRAM Chips and Implications on Future Attacks and Defenses, [link to arXiv](https://arxiv.org/abs/2110.10291) +- Understanding RowHammer Under Reduced Wordline Voltage: An Experimental Study Using Real DRAM Devices, [link to arXiv](https://arxiv.org/abs/2206.09999) +- TRRespass: Exploiting the Many Sides of Target Row Refresh, [link to arXiv](https://arxiv.org/abs/2004.01807), proof of concept already available [on Github](https://github.com/vusec/trrespass) +- EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM, [link to arXiv](https://arxiv.org/abs/1910.05340) + ## Repository File Structure ``` |
