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ISCA2024顶会

Native DRAM Cache: Re-architecting DRAM as a Large-Scale Cache for Data Centers

Yesin Ryu, Yoojin Kim, Giyong Jung, Jung Ho Ahn, Jungrae Kim

2024年份
5被引次数
2顶会引用

摘要

Contemporary data center CPUs are experiencing an unprecedented surge in core count. This trend necessitates scrutinized Last-Level Cache (LLC) strategies to accommodate increasing capacity demands. While DRAM offers significant capacity, using it as a cache poses challenges related to latency and energy. This paper introduces Native DRAM Cache (NDC), a novel DRAM architecture specifically designed to operate as a cache. NDC features innovative approaches, such as conducting tag matching and way selection within a DRAM subarray and repurposing existing precharge transistors for tag matching. These innovations facilitate Caching-In-Memory (CIM) and enable NDC to serve as a high-capacity LLC with high set-associativity, low-latency, high-throughput, and low-energy. Our evaluation demonstrates that NDC significantly outperforms state-of-the-art DRAM cache solutions, enhancing performance by 2.8%/52.5%/44.2%\mathbf{2.8 \%} / \mathbf{52.5 \%} / \mathbf{44.2 \%} (up to 8.4%/140.6%/85.5%8.4 \% / 140.6 \% / 85.5 \%) in SPEC/NPB/GAP benchmark suites, respectively.

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