research-article

An FPGA-based accelerator for LambdaRank in Web search engines

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Feng-HSIUNG HsuAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 4, Issue 3

Article No.: 25, Pages 1 - 19

https://doi.org/10.1145/2000832.2000837

Published: 22 August 2011 Publication History

Abstract

In modern Web search engines, Neural Network (NN)-based learning to rank algorithms is intensively used to increase the quality of search results. LambdaRank is one such algorithm. However, it is hard to be efficiently accelerated by computer clusters or GPUs, because: (i) the cost function for the ranking problem is much more complex than that of traditional Back-Propagation(BP) NNs, and (ii) no coarse-grained parallelism exists in the algorithm. This article presents an FPGA-based accelerator solution to provide high computing performance with low power consumption. A compact deep pipeline is proposed to handle the complex computing in the batch updating. The area scales linearly with the number of hidden nodes in the algorithm. We also carefully design a data format to enable streaming consumption of the training data from the host computer. The accelerator shows up to 15.3X (with PCIe x4) and 23.9X (with PCIe x8) speedup compared with the pure software implementation on datasets from a commercial search engine.

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Cited By

Li QFleming SThomas DCheung P(2018)Accelerating Top-k ListNet Training for Ranking Using FPGA2018 International Conference on Field-Programmable Technology (FPT)10.1109/FPT.2018.00044(242-245)Online publication date: Dec-2018
https://doi.org/10.1109/FPT.2018.00044
Nakano MYamaguchi Y(2017)A Study of TRAX Player by Template Matching2017 Fifth International Symposium on Computing and Networking (CANDAR)10.1109/CANDAR.2017.33(570-574)Online publication date: Nov-2017
https://doi.org/10.1109/CANDAR.2017.33
Tan ESeaman ILeung HNg YAnderst-Kotsis G(2016)Making personalized movie recommendations for childrenProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services10.1145/3011141.3011142(96-105)Online publication date: 28-Nov-2016
https://dl.acm.org/doi/10.1145/3011141.3011142

Index Terms

An FPGA-based accelerator for LambdaRank in Web search engines
1. Hardware
  1. Very large scale integration design
    1. Application-specific VLSI designs
      1. Application specific processors

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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 4, Issue 3

August 2011

204 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/2000832

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2011

Accepted: 01 August 2010

Revised: 01 August 2010

Received: 01 April 2010

Published in TRETS Volume 4, Issue 3

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Cited By

Li QFleming SThomas DCheung P(2018)Accelerating Top-k ListNet Training for Ranking Using FPGA2018 International Conference on Field-Programmable Technology (FPT)10.1109/FPT.2018.00044(242-245)Online publication date: Dec-2018
https://doi.org/10.1109/FPT.2018.00044
Nakano MYamaguchi Y(2017)A Study of TRAX Player by Template Matching2017 Fifth International Symposium on Computing and Networking (CANDAR)10.1109/CANDAR.2017.33(570-574)Online publication date: Nov-2017
https://doi.org/10.1109/CANDAR.2017.33
Tan ESeaman ILeung HNg YAnderst-Kotsis G(2016)Making personalized movie recommendations for childrenProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services10.1145/3011141.3011142(96-105)Online publication date: 28-Nov-2016
https://dl.acm.org/doi/10.1145/3011141.3011142

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