DRAMDig: A Knowledge-assisted Tool to Uncover DRAM Address Mapping

Minghua Wang, Zhi Zhang, Yueqiang Cheng, Surya Nepal

Research output: Chapter in Book/Conference paperConference paperpeer-review

32 Citations (Scopus)

Abstract

As recently emerged rowhammer exploits require undocumented DRAM address mapping, we propose a generic knowledge-assisted tool, DRAMDig, which takes domain knowledge into consideration to efficiently and deterministically uncover the DRAM address mappings on any Intel-based machines. We test DRAMDig on a number of machines with different combinations of DRAM chips and microarchitectures ranging from Intel Sandy Bridge to Coffee Lake. Comparing to previous works, DRAMDig deterministically reverse-engineered DRAM address mappings on all the test machines with only 7.8 minutes on average. Based on the uncovered mappings, we perform double-sided rowhammer tests and the results show that DRAMDig induced significantly more bit flips than previous works, justifying the correctness of the uncovered DRAM address mappings.
Original languageEnglish
Title of host publication2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781450367257
DOIs
Publication statusPublished - 23 Jul 2020
Externally publishedYes
Event57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
Duration: 20 Jul 202024 Jul 2020
Conference number: 57

Publication series

NameProceedings - Design Automation Conference
Volume2020-July
ISSN (Print)0738-100X

Conference

Conference57th ACM/IEEE Design Automation Conference, DAC 2020
Abbreviated titleACM/IEEE DAC
Country/TerritoryUnited States
CityVirtual, San Francisco
Period20/07/2024/07/20

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