Simultaneous Many-Row Activation in Off-the-Shelf DRAM Chips: Experimental Characterization and Analysis
<|reference_start|>Simultaneous Many-Row Activation in Off-the-Shelf DRAM Chips: Experimental Characterization and Analysis: We experimentally analyze the computational capability of commercial off-the-shelf (COTS) DRAM chips and the robustness of these capabilities under various timing delays between DRAM commands, data patterns, temperature, and voltage levels. We extensively characterize 120 COTS DDR4 chips from two major manufacturers. We highlight four key results of our study. First, COTS DRAM chips are capable of 1) simultaneously activating up to 32 rows (i.e., simultaneous many-row activation), 2) executing a majority of X (MAJX) operation where X>3 (i.e., MAJ5, MAJ7, and MAJ9 operations), and 3) copying a DRAM row (concurrently) to up to 31 other DRAM rows, which we call Multi-RowCopy. Second, storing multiple copies of MAJX's input operands on all simultaneously activated rows drastically increases the success rate (i.e., the percentage of DRAM cells that correctly perform the computation) of the MAJX operation. For example, MAJ3 with 32-row activation (i.e., replicating each MAJ3's input operands 10 times) has a 30.81% higher average success rate than MAJ3 with 4-row activation (i.e., no replication). Third, data pattern affects the success rate of MAJX and Multi-RowCopy operations by 11.52% and 0.07% on average. Fourth, simultaneous many-row activation, MAJX, and Multi-RowCopy operations are highly resilient to temperature and voltage changes, with small success rate variations of at most 2.13% among all tested operations. We believe these empirical results demonstrate the promising potential of using DRAM as a computation substrate. To aid future research and development, we open-source our infrastructure at https://github.com/CMU-SAFARI/SiMRA-DRAM.<|reference_end|>
@article{yuksel2024simultaneous,
    title={Simultaneous Many-Row Activation in Off-the-Shelf DRAM Chips:
  Experimental Characterization and Analysis},
    author={Ismail Emir Yuksel, Yahya Can Tugrul, F. Nisa Bostanci, Geraldo F.
  Oliveira, A. Giray Yaglikci, Ataberk Olgun, Melina Soysal, Haocong Luo, Juan
  G'omez-Luna, Mohammad Sadrosadati, Onur Mutlu},
    journal={arXiv preprint arXiv:2405.06081},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06081},
    primaryClass={cs.AR cs.DC}
}