Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Abdelhadi:2020:10.1109/ICFPT47387.2019.00019,
author = {Abdelhadi, A and Bouganis, C and Constantinides, G},
doi = {10.1109/ICFPT47387.2019.00019},
pages = {1--9},
publisher = {IEEE},
title = {Accelerated approximate nearest neighbors search through hierarchical product quantization},
url = {http://dx.doi.org/10.1109/ICFPT47387.2019.00019},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - A  fundamental  recurring  task  in  many  machinelearning  applications  is  the  search  for  the  Nearest  Neighbor  inhigh  dimensional  metric  spaces.  Towards  answering  queries  inlarge  scale  problems,  state-of-the-art  methods  employ  Approx-imate  Nearest  Neighbors  (ANN)  search,  a  search  that  returnsthe nearest neighbor with high probability, as well as techniquesthat compress the dataset. Product-Quantization (PQ) based ANNsearch methods have demonstrated state-of-the-art performancein several problems, including classification, regression and infor-mation retrieval. The dataset is encoded into a Cartesian productof  multiple  low-dimensional  codebooks,  enabling  faster  searchand  higher  compression.  Being  intrinsically  parallel,  PQ-basedANN  search  approaches  are  amendable  for  hardware  accelera-tion. This paper proposes a novel Hierarchical PQ (HPQ) basedANN  search  method  as  well  as  an  FPGA-tailored  architecturefor  its  implementation  that  outperforms  current  state  of  theart  systems.  HPQ  gradually  refines  the  search  space,  reducingthe  number  of  data  compares  and  enabling  a  pipelined  search.The  mapping  of  the  architecture  on  a  Stratix  10  FPGA  devicedemonstrates  over×250  speedups  over  current  state-of-the-artsystems, opening the space for addressing larger datasets and/orimproving  the  query  times  of  current  systems.
AU  - Abdelhadi,A
AU  - Bouganis,C
AU  - Constantinides,G
DO  - 10.1109/ICFPT47387.2019.00019
EP  - 9
PB  - IEEE
PY  - 2020///
SP  - 1
TI  - Accelerated approximate nearest neighbors search through hierarchical product quantization
UR  - http://dx.doi.org/10.1109/ICFPT47387.2019.00019
UR  - https://ieeexplore.ieee.org/document/8977838
UR  - http://hdl.handle.net/10044/1/75443
ER  -
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