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NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules
Abstract
Energy consumed for transferring data across the processor memory hierarchy constitutes a large fraction of total system energy consumption, and this fraction has steadily increased with technology scaling. In this paper, we propose near-DRAM acceleration (NDA) architectures, which process data using accelerators 3D-stacked on DRAM devices comprising off-chip main memory modules. NDA transfers most data through high-bandwidth and low-energy 3D interconnects between accelerators and DRAM devices instead of low-bandwidth and high-energy off-chip interconnects between a processor and DRAM devices, substantially reducing energy consumption and improving performance. Unlike previous near-memory processing architectures, NDA is built upon commodity DRAM devices; apart from inserting through-silicon vias (TSVs) to 3D-interconnect DRAM devices and accelerators, NDA requires minimal changes to the commodity DRAM device and standard memory module architectures. This allows NDA to be more easily adopted in both existing and emerging systems. Our experiments demonstrate that, on average, our NDA-based system consumes 46% (68%) lower (data transfer) energy at 1.67× higher performance than a system that integrates the same accelerator logic within the processor itself.
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Binkert, N., Beckmann, B., Black, G., Reinhardt, S. K., Saidi, A., Basu, A., Hestness, J., Hower, D. R., Krishna, T., Sardashti, S., et al. (2011) · ACM SIGARCH Computer Architecture News · doi:10.1145/2024716.2024718 · Cited by 4,930
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Song, L., Qian, X., Li, H., & Chen, Y. (2017) · 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2017.55 · Cited by 833
Computing in Memory With Spin-Transfer Torque Magnetic RAM
Jain, S., Ranjan, A., Roy, K., & Raghunathan, A. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2017.2776954 · Cited by 355
Practical Near-Data Processing for In-Memory Analytics Frameworks
Gao, M., Ayers, G., & Kozyrakis, C. (2015) · 2015 International Conference on Parallel Architecture and Compilation (PACT) · doi:10.1109/pact.2015.22 · Cited by 266
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
- Farmahini-Farahani, A., Ahn, J. H., Morrow, K., & Kim, N. S.
- Venue
- 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)
- Publisher
- IEEE
- Published
- 2015-02
- Pages
- 283-295
- ISSN
- 1530-0897 (ISSN-L)
- OpenAlex
- W2045431893
- Cited by
- 274
- References
- 1
- FWCI
- 34.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Farmahini-Farahani, A., Ahn, J. H., Morrow, K., & Kim, N. S. (2015). NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules. 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA). https://doi.org/10.1109/hpca.2015.7056040
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