{"title":"FANNS: An FPGA-Based Approximate Nearest-Neighbor Search Accelerator","authors":"Wei Yuan;Xi Jin","doi":"10.1109/TVLSI.2024.3496589","DOIUrl":null,"url":null,"abstract":"Approximate nearest-neighbor search (ANNS) based on high-dimensional vectors has been extensively utilized in data science and neural networks. However, deploying ANNS in production systems requires minimal redundant computation, high recall rates, and low on-chip memory usage, which existing hardware accelerators fail to offer. We propose FANNS, a solution for ANNS based on high-dimensional vectors that can eliminate redundant computations and reuse on-chip data. Extensive evaluations show that FANNS achieves an average of <inline-formula> <tex-math>$184.1\\times $ </tex-math></inline-formula>, <inline-formula> <tex-math>$33.0\\times $ </tex-math></inline-formula>, <inline-formula> <tex-math>$2.9\\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math>$2.5\\times $ </tex-math></inline-formula> better energy efficiency than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., DF-GAS and Vstore, respectively.","PeriodicalId":13425,"journal":{"name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","volume":"33 4","pages":"1197-1201"},"PeriodicalIF":2.8000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10885776/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Approximate nearest-neighbor search (ANNS) based on high-dimensional vectors has been extensively utilized in data science and neural networks. However, deploying ANNS in production systems requires minimal redundant computation, high recall rates, and low on-chip memory usage, which existing hardware accelerators fail to offer. We propose FANNS, a solution for ANNS based on high-dimensional vectors that can eliminate redundant computations and reuse on-chip data. Extensive evaluations show that FANNS achieves an average of $184.1\times $ , $33.0\times $ , $2.9\times $ , and $2.5\times $ better energy efficiency than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., DF-GAS and Vstore, respectively.
期刊介绍:
The IEEE Transactions on VLSI Systems is published as a monthly journal under the co-sponsorship of the IEEE Circuits and Systems Society, the IEEE Computer Society, and the IEEE Solid-State Circuits Society.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
To address this critical area through a common forum, the IEEE Transactions on VLSI Systems have been founded. The editorial board, consisting of international experts, invites original papers which emphasize and merit the novel systems integration aspects of microelectronic systems including interactions among systems design and partitioning, logic and memory design, digital and analog circuit design, layout synthesis, CAD tools, chips and wafer fabrication, testing and packaging, and systems level qualification. Thus, the coverage of these Transactions will focus on VLSI/ULSI microelectronic systems integration.