The DOI matches its Crossref record.
Hybrid memory cube new DRAM architecture increases density and performance
Abstract
Multi-core processor performance is limited by memory system bandwidth. The Hybrid Memory Cube is a three-dimensional DRAM architecture that improves latency, bandwidth, power and density. Through-silicon vias (TSVs), 3D packaging and advanced CMOS performance enable a new approach to memory system architecture. Heterogeneous die are stacked with significantly more connections, thereby reducing the distance signals travel.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
“le of using eDRAM for DNN processing can be found in DaDianNao [89], which uses 36MB of eDRAM to store the weights for the fully-connected layers. 3-D memory, such as Micron’s Hyper Memory Cube (HMC) [94], stacks the DRAM on top of the logic using through silicon vias (TSV); this results in an order of magnitude highe…”
MethodAn overview of through-silicon-via technology and manufacturing challenges
Gambino, J. P., Adderly, S. A., & Knickerbocker, J. U. (2015) · Microelectronic Engineering · doi:10.1016/j.mee.2014.10.019 · Cited by 317
Deep Learning on Mobile and Embedded Devices: State-of-the-art, Challenges, and Future Directions
Chen, Y., Zheng, B., Zhang, Z., Wang, Q., Shen, C., & Zhang, Q. (2021) · ACM Computing Surveys · doi:10.1145/3398209 · Cited by 159
Data-Centric Computing Frontiers: A Survey On Processing-In-Memory
Siegl, P., Buchty, R., & Berekovic, M. (2016) · Proceedings of the Second International Symposium on Memory Systems · doi:10.1145/2989081.2989087 · Cited by 55
Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM
Seshadri, V., Lee, D., Mullins, T., Hassan, H., Boroumand, A., Kim, J., Kozuch, M. A., Mutlu, O., Gibbons, P. B., & Mowry, T. C. (2016) · arXiv · doi:10.48550/arxiv.1611.09988 · Cited by 54
Metadata
- Authors
- Jeddeloh, J., & Keeth, B.
- Venue
- 2012 Symposium on VLSI Technology (VLSIT)
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 87-88
- OpenAlex
- W1981220134
- Cited by
- 444
- References
- 0
- FWCI
- 38.48
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hybrid memory cube new DRAM architecture increases density and performance. https://app.cochat.ai/papers/10.1109/vlsit.2012.6242474
- The paper
- Jeddeloh, J., & Keeth, B. (2012). Hybrid memory cube new DRAM architecture increases density and performance. 2012 Symposium on VLSI Technology (VLSIT). https://doi.org/10.1109/vlsit.2012.6242474
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.