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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09605 |
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| _version_ | 1866917893886705664 |
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| author | Legtchenko, Sergey Stefanovici, Ioan Black, Richard Rowstron, Antony Liu, Junyi Costa, Paolo Canakci, Burcu Narayanan, Dushyanth Wu, Xingbo |
| author_facet | Legtchenko, Sergey Stefanovici, Ioan Black, Richard Rowstron, Antony Liu, Junyi Costa, Paolo Canakci, Burcu Narayanan, Dushyanth Wu, Xingbo |
| contents | AI clusters today are one of the major uses of High Bandwidth Memory (HBM). However, HBM is suboptimal for AI workloads for several reasons. Analysis shows HBM is overprovisioned on write performance, but underprovisioned on density and read bandwidth, and also has significant energy per bit overheads. It is also expensive, with lower yield than DRAM due to manufacturing complexity. We propose a new memory class: Managed-Retention Memory (MRM), which is more optimized to store key data structures for AI inference workloads. We believe that MRM may finally provide a path to viability for technologies that were originally proposed to support Storage Class Memory (SCM). These technologies traditionally offered long-term persistence (10+ years) but provided poor IO performance and/or endurance. MRM makes different trade-offs, and by understanding the workload IO patterns, MRM foregoes long-term data retention and write performance for better potential performance on the metrics important for these workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Managed-Retention Memory: A New Class of Memory for the AI Era Legtchenko, Sergey Stefanovici, Ioan Black, Richard Rowstron, Antony Liu, Junyi Costa, Paolo Canakci, Burcu Narayanan, Dushyanth Wu, Xingbo Hardware Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Emerging Technologies AI clusters today are one of the major uses of High Bandwidth Memory (HBM). However, HBM is suboptimal for AI workloads for several reasons. Analysis shows HBM is overprovisioned on write performance, but underprovisioned on density and read bandwidth, and also has significant energy per bit overheads. It is also expensive, with lower yield than DRAM due to manufacturing complexity. We propose a new memory class: Managed-Retention Memory (MRM), which is more optimized to store key data structures for AI inference workloads. We believe that MRM may finally provide a path to viability for technologies that were originally proposed to support Storage Class Memory (SCM). These technologies traditionally offered long-term persistence (10+ years) but provided poor IO performance and/or endurance. MRM makes different trade-offs, and by understanding the workload IO patterns, MRM foregoes long-term data retention and write performance for better potential performance on the metrics important for these workloads. |
| title | Managed-Retention Memory: A New Class of Memory for the AI Era |
| topic | Hardware Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Emerging Technologies |
| url | https://arxiv.org/abs/2501.09605 |