@article{gao2009fpga,
author = {Gao, Rui and Zhang, Lei and Hsu, Feng-Hsiung},
title = {FPGA Acceleration of RankBoost in Web Search Engines},
year = {2009},
month = {January},
abstract = {Search relevance is a key measurement for the usefulness of search engines. Shift of search relevance among search engines can easily change a search company's market cap by tens of billions of dollars. With the ever-increasing scale of the Web, machine learning technologies have become important tools to improve search relevance ranking. RankBoost is a promising algorithm in this area, but it is not widely used due to its long training time. To reduce the computation time for RankBoost, we designed a FPGA-based accelerator system and its upgraded version. The accelerator, plugged into a commodity PC, increased the training speed on MSN search engine data up to 1800x compared to the original software implementation on a server. The proposed accelerator has been successfully used by researchers in the search relevance ranking.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/fpga-acceleration-of-rankboost-in-web-search-engines/},
pages = {1-19},
journal = {ACM Trans. Reconfigurable Technol. Syst.},
volume = {1},
edition = {ACM Trans. Reconfigurable Technol. Syst.},
}