CLR-DRAM: A Low-Cost DRAM Architecture Enabling Dynamic Capacity-Latency Trade-Off
Haocong Luo, Taha Shahroodi, Hasan Hassan, Minesh Patel, Abdullah Giray Yaglikçi, Lois Orosa, Jisung Park, Onur Mutlu
Abstract
DRAM is the prevalent main memory technology, but its long access latency can limit the performance of many workloads. Although prior works provide DRAM designs that reduce DRAM access latency, their reduced storage capacities hinder the performance of workloads that need large memory capacity. Because the capacity-latency trade-off is fixed at design time, previous works cannot achieve maximum performance under very different and dynamic workload demands.
This paper proposes Capacity-Latency-Reconfigurable DRAM (CLR-DRAM), a new DRAM architecture that enables dynamic capacity-latency trade-off at low cost. CLR-DRAM allows dynamic reconfiguration of any DRAM row to switch between two operating modes: 1) max-capacity mode, where every DRAM cell operates individually to achieve approximately the same storage density as a density-optimized commodity DRAM chip and 2) high-performance mode, where two adjacent DRAM cells in a DRAM row and their sense amplifiers are coupled to operate as a single low-latency logical cell driven by a single logical sense amplifier.
We implement CLR-DRAM by adding isolation transistors in each DRAM subarray. Our evaluations show that CLR-DRAM can improve system performance and DRAM energy consumption by 18.6% and 29.7% on average with four-core multiprogrammed workloads. We believe that CLR-DRAM opens new research directions for a system to adapt to the diverse and dynamically changing memory capacity and access latency demands of workloads.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 0424bdc7-4ec0-48d6-98ed-ee1341d25365Cited by top-tier papers20
- BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM RowsAbdullah Giray Yaglikçi, Minesh Patel, Jeremie S. Kim, Roknoddin Azizi et al.HPCA 2021 · 124 citations
- Uncovering In-DRAM RowHammer Protection Mechanisms: A New Methodology, Custom RowHammer Patterns, and ImplicationsHasan Hassan, Yahya Can Tugrul, Jeremie S. Kim, Victor van der Veen et al.MICRO 2021 · 79 citations
- A Deeper Look into RowHammer's Sensitivities: Experimental Analysis of Real DRAM Chipsand Implications on Future Attacks and DefensesLois Orosa, Abdullah Giray Yaglikçi, Haocong Luo, Ataberk Olgun et al.MICRO 2021 · 74 citations
- FIGARO: Improving System Performance via Fine-Grained In-DRAM Data Relocation and CachingYaohua Wang, Lois Orosa, Xiangjun Peng, Yang Guo et al.MICRO 2020 · 72 citations
- Reducing solid-state drive read latency by optimizing read-retryJisung Park, Myungsuk Kim, Myoungjun Chun, Lois Orosa et al.ASPLOS 2021 · 66 citations
Builds on2
- DRAMA: Exploiting DRAM Addressing for Cross-CPU AttacksPeter Pessl, Daniel Gruss, Clémentine Maurice, Michael Schwarz et al.USENIX Security 2016 · 500 citations
- The Virtual Block Interface: A Flexible Alternative to the Conventional Virtual Memory FrameworkNastaran Hajinazar, Pratyush Patel, Minesh Patel, Konstantinos Kanellopoulos et al.ISCA 2020 · 25 citations
Related papers
- Agile-DRAM: Agile Trade-Offs in Memory Capacity, Latency, and Energy for Data CentersJaeyoon Lee, Wonyeong Jung, Dongwhee Kim, Daero Kim et al.HPCA 2024 · 4 citations
- Reducing DRAM Access Latency via Helper RowsXin Xin, Youtao Zhang, Jun YangDAC 2020 · 10 citations
- CDAR-DRAM: An In-situ Charge Detection and Adaptive Data Restoration DRAM Architecture for Performance and Energy Efficiency ImprovementChuxiong Lin, Weifeng He, Yanan Sun, Zhigang Mao et al.DAC 2021 · 6 citations
- DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural NetworksRachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad ShafiqueDAC 2020 · 36 citations
- HiRA: Hidden Row Activation for Reducing Refresh Latency of Off-the-Shelf DRAM ChipsAbdullah Giray Yaglikçi, Ataberk Olgun, Minesh Patel, Haocong Luo et al.MICRO 2022 · 43 citations
