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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. The accelerator, plugged into a commodity PC, increased the training speed on MSN search engine data by 2 orders of magnitude compared to the original software implementation on a server. The proposed accelerator has been successfully used by researchers in the search relevance ranking .