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Exploring Pareto-Optimal Hybrid Main Memory Configurations Using Different Emerging Memories

Main memory system design and corresponding technology requirements have become increasingly challenging for data-dominated high-performance applications. To address the leakage and scalability issues of the conventional DRAM-based memory, new memory technologies with ultra-low leakage and potential...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2023-02, Vol.70 (2), p.733-746
Main Authors: Alinezhad Chamazcoti, Saeideh, Gupta, Mohit, Oh, Hyungrock, Evenblij, Timon, Catthoor, Francky, Komalan, Manu Perumkunnil, Kar, Gouri Sankar, Furnemont, Arnaud
Format: Article
Language:English
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Summary:Main memory system design and corresponding technology requirements have become increasingly challenging for data-dominated high-performance applications. To address the leakage and scalability issues of the conventional DRAM-based memory, new memory technologies with ultra-low leakage and potential for high scalability have been explored extensively over the last decade. However, none of them are mature enough to serve as a drop-in replacement for DRAM. In this paper, we propose a hybrid main memory system solution for utilizing new memory technologies with specific features, based on the target application characteristics and system configurations. To this end, we examine two new memories, 1S-1VCMA and IGZO-based DRAM, along with conventional DRAM in the context of hybrid main memory solutions for high-capacity and low-power Pareto-optimizations, respectively. To better evaluate the power and performance, we consider the page-fault modeling in our evaluations. The results of the simulation show that different combinations of memory technologies in the hybrid memory system, different memory capacities, and different storage systems could provide a promising solution in the system regarding the characteristics of running applications and the requirements of the system.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2022.3222573