Paper Info
Title
Hardware-Accelerated Similarity Search With Multi-Index Hashing
Abstract
Similarity search plays a major role in a large variety of database applications where high-dimensional data, usually derived from images and videos, are mapped onto indexed structures. Its essence relies on a k-nearest-neighbors (kNN) algorithm to find similar contents given a specific input data. An efficient way to store and speedup such searches is to apply kNN in Hamming space by encoding the data as binary and splitting it into multiple hash tables. This paper proposes an efficient Hardware-Accelerated Similarity Search co-processor architecture using Multi-Index Hashing as storage structure. The accelerator is specified in Verilog hardware description language and implemented in a Xilinx low-cost Zynq FPGA. Performance, circuit-area and power consumption results are presented, with the accelerator being up to 13x and 18x faster than the corresponding C/C++ code on the ARM host processor when running the SIFT and GIST datasets, respectively, while also requiring less power.
Year
DOI
Venue
2019
10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00138
IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH)
Field
DocType
Citations 
Computer science,Binary code,Parallel computing,Field-programmable gate array,Hamming distance,Hash function,Hamming space,Nearest neighbor search,Hash table,Speedup
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Leandro Santiago de Araújo132.43
Victor da Cruz Ferreira200.34
Brunno Figueiroa Goldstein300.34
Alexandre Solon Nery400.34
Leandro Augusto Justen Marzulo500.34
Sandip Kundu61103137.18
Felipe Maia Galvão França7155.03