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VStore: in-storage graph based vector search accelerator

Published: 23 August 2022 Publication History

Abstract

Graph-based vector search that finds best matches to user queries based on their semantic similarities using a graph data structure, becomes instrumental in data science and AI application. However, deploying graph-based vector search in production systems requires high accuracy and cost-efficiency with low latency and memory footprint, which existing work fails to offer. We present VStore, a graph-based vector search solution that collaboratively optimizes accuracy, latency, memory, and data movement on large-scale vector data based on in-storage computing. The evaluation shows that VStore exhibits significant search efficiency improvement and energy reduction while attaining accuracy over CPU, GPU, and ZipNN platforms.

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Cited By

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  • (2024)Scalable billion-point approximate nearest neighbor search using SmartSSDsProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692061(1135-1150)Online publication date: 10-Jul-2024
  • (2024)HyQA: Hybrid Near-Data Processing Platform for Embedding Based Question Answering System2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546723(1-6)Online publication date: 25-Mar-2024
  • (2024)Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor SearchACM Transactions on Storage10.1145/363947120:2(1-30)Online publication date: 19-Feb-2024
  • Show More Cited By

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cover image ACM Conferences
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
July 2022
1462 pages
ISBN:9781450391429
DOI:10.1145/3489517
This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

Publication History

Published: 23 August 2022

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DAC '22
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DAC '22: 59th ACM/IEEE Design Automation Conference
July 10 - 14, 2022
California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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View all
  • (2024)Scalable billion-point approximate nearest neighbor search using SmartSSDsProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692061(1135-1150)Online publication date: 10-Jul-2024
  • (2024)HyQA: Hybrid Near-Data Processing Platform for Embedding Based Question Answering System2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546723(1-6)Online publication date: 25-Mar-2024
  • (2024)Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor SearchACM Transactions on Storage10.1145/363947120:2(1-30)Online publication date: 19-Feb-2024
  • (2024)NICE: A Nonintrusive In-Storage-Computing Framework for Embedded ApplicationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344685743:11(3876-3887)Online publication date: Nov-2024
  • (2023)DF-GAS: a Distributed FPGA-as-a-Service Architecture towards Billion-Scale Graph-based Approximate Nearest Neighbor SearchProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614292(283-296)Online publication date: 28-Oct-2023

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