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arXiv · 2505.16096

Cosmos: A CXL-Based Full In-Memory System for Approximate Nearest Neighbor Search

Seoyoung Ko·Hyunjeong Shim·Wanju Doh·Sungmin Yun·Jinin So·Yongsuk Kwon·Sang-Soo Park·Si-Dong Roh·Minyong Yoon·Taeksang Song·Jung Ho Ahn
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Abstract

Retrieval-Augmented Generation (RAG) is crucial for improving the quality of large language models by injecting proper contexts extracted from external sources. RAG requires high-throughput, low-latency Approximate Nearest Neighbor Search (ANNS) over billion-scale vector databases. Conventional DRAM/SSD solutions face capacity/latency limits, whereas specialized hardware or RDMA clusters lack flexibility or incur network overhead. We present Cosmos, integrating general-purpose cores within CXL memory devices for full ANNS offload and introducing rank-level parallel distance computation to maximize memory bandwidth. We also propose an adjacency-aware data placement that balances search loads across CXL devices based on inter-cluster proximity. Evaluations on SIFT1B and DEEP1B traces show that Cosmos achieves up to 6.72x higher throughput than the baseline CXL system and 2.35x over a state-of-the-art CXL-based solution, demonstrating scalability for RAG pipelines.

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Seoyoung Ko, Hyunjeong Shim, Wanju Doh, Sungmin Yun, Jinin So, Yongsuk Kwon, Sang-Soo Park, Si-Dong Roh, Minyong Yoon, Taeksang Song, Jung Ho Ahn. 2025-05-22. Cosmos: A CXL-Based Full In-Memory System for Approximate Nearest Neighbor Search. https://doi.org/10.1109/lca.2025.3570235

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