ReScue: Reliable and Secure CXL Memory
Chihun Song, Austin Antony Cruz, Michael Jaemin Kim, Minbok Wi, Gaohan Ye, Kyungsan Kim, Sangyeol Lee, Jung Ho Ahn, Nam Sung Kim
摘要
Compute Express Link (CXL) fundamentally shifts the memory abstraction boundary, moving memory management functions from the host CPU to external controllers. While this decoupling allows hyperscalers to integrate diverse media, such as cost-effective, recycled DDR4 modules, it transforms the memory controller into a complex system with new integration challenges. First, we uncover that integrating CXL and memory controller IPs via the industry-standard AXI interconnect creates critical bottlenecks. We demonstrate that variable access latency and out-of-order responses, essential for CXL, can quickly saturate the limited AXI tag space, limiting bandwidth and even causing system hangs in commercial platforms. However, this decoupled architecture also creates a unique opportunity: the CXL controller can exploit its support for variable-latency responses and near-memory processing to implement transparent reliability and security mechanisms. We propose ReScue, a suite of hardware solutions implemented and validated on an Intel Agilex platform. ReScue-R leverages CXL's variable access latency and out-of-order responses to transparently retrieve remapping addresses stored within faulty blocks. By utilizing a Bloom filter to predict faults and minimize overhead, it achieves fault tolerancetimes higher than standard SECDED ECC with only 0.2% performance degradation. ReScue-S addresses security; after demonstrating the first successful Row Hammer (RH) bit flips and physical-address reconstruction on a CXL system, we show that ReScue-S can shape response latencies to prevent timing-based side-channel attacks with only 1.1% performance degradation. Finally, we propose a solution to the discovered AXI-induced system hangs to ensure stable CXL operation.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- CXL-ECC: an Efficient LRC-based on-CXL-Memory-eXpander-Controller ECC to Enhance Reliability and Performance of DRAM Error CorrectionYixuan Liu, Yunfei Gu, Junhao Dai, Xinyuan Wu 等DAC 2025
- MAC: Metadata Acceleration for Sustainable Performance in Big-Data Systems with CXL DRAMDusol Lee, Yan Sun, Houxiang Ji, Vinit Gupta 等OSDI 2026
- Demystifying CXL Memory with Genuine CXL-Ready Systems and DevicesYan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper 等MICRO 2023 · 被引用 133 次
- CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data CentersJian Chen, Xiaowei Jiang, Ying Zhang, Liyin Liu 等HPCA 2021 · 被引用 9 次
- CXL-INTERPLAY: Unraveling and Characterizing CXL Interference in Modern Computer SystemsShunyu Mao, Jiajun Luo, Yixin Li, Jiapeng Zhou 等DAC 2025 · 被引用 4 次
