TDRAM: Tag-enhanced DRAM for Efficient Caching

Maryam Babaie, Ayaz Akram, Wendy Elsasser, Brent Haukness, Michael R. Miller, Taeksang Song, Thomas Vogelsang, Steven C. Woo, Jason Lowe-Power

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As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demands. In this paper, we propose TDRAM, a novel DRAM microarchitecture tailored for caching. TDRAM enhances existing DRAM, such as HBM3, by adding small, low-latency mats to store tags and metadata on the same die as the data mats. These mats enable tag and data access in lockstep, in-DRAM tag comparison, and conditional data response based on the comparison result (reducing wasted data transfers), akin to SRAM cache mechanisms. TDRAM further optimizes hit and miss latencies through opportunistic early tag probing. Moreover, TDRAM introduces a flush buffer to store conflicting dirty data on write misses, eliminating data bus turnaround delays on write demands. We evaluate TDRAM in a full-system simulation using a set of HPC workloads with large memory footprints, showing that TDRAM, on average, provides 2.65× faster tag checks, 1.23× speedup, and 21% less energy consumption compared to state-of-the-art commercial and research designs.

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Maryam Babaie, Ayaz Akram, Wendy Elsasser, Brent Haukness, Michael R. Miller, Taeksang Song, Thomas Vogelsang, Steven C. Woo, and Jason Lowe-Power, “TDRAM: Tag-enhanced DRAM for Efficient Caching,” arXiv preprint arXiv:2404.14617, 2024. doi: 10.48550/arXiv.2404.14617.

@article{babaie2024tdram,
  author       = {Maryam Babaie and Ayaz Akram and Wendy Elsasser and Brent Haukness and Michael R. Miller and Taeksang Song and Thomas Vogelsang and Steven C. Woo and Jason Lowe-Power},
  title        = {TDRAM: Tag-enhanced DRAM for Efficient Caching},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2404.14617},
  doi          = {10.48550/arXiv.2404.14617},
  eprint={2404.14617},
  archivePrefix={arXiv},
  primaryClass={cs.AR}
}

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