High throughput filtering using FPGA-acceleration
Authors: 
Wim Vanderbauwhede, Anton Frolov, 
Leif Azzopardi, Sai Rahul Chalamalasetti, Martin MargalaAuthors Info & Claims
Wim Vanderbauwhede, Anton Frolov, Leif Azzopardi, Sai Rahul Chalamalasetti, Martin MargalaAuthors Info & ClaimsAbstract
With the rise in the amount information of being streamed across networks, there is a growing demand to vet the quality, type and content itself for various purposes such as spam, security and search. In this paper, we develop an energy-efficient high performance information filtering system that is capable of classifying a stream of incoming document at high speed. The prototype parses a stream of documents using a multicore CPU and then performs classification using Field-Programmable Gate Arrays (FPGAs). On a large TREC data collection, we implemented a Naive Bayes classifier on our prototype and compared it to an optimized CPU based-baseline. Our empirical findings show that we can classify documents at 10Gb/s which is up to 94 times faster than the CPU baseline (and up to 5 times faster than previous FPGA based implementations). In future work, we aim to increase the throughput by another order of magnitude by implementing both the parser and filter on the FPGA.
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Information & Contributors
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Published In
October 2013
2612 pages
ISBN:9781450322638
DOI:10.1145/2505515
- General Chairs:
- Qi He,
- Arun Iyengar,
- Program Chairs:
- Wolfgang Nejdl,
- Jian Pei,
- Rajeev Rastogi
Copyright © 2013.
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Published: 27 October 2013
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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
October 27 - November 1, 2013
California, San Francisco, USA
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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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