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Web search engine companies are intensively running learning to rank algorithms to improve the search relevance. Neural network (NN)-based approaches, such as LambdaRank, can significantly increase the ranking quality. While, their training is very slow on a single computer and inherent coarse-grained parallelism could be hardly utilized by computer clusters. Thus an efficient implementation is necessary...
Web search engine ranks web pages according to their relevance to user queries, which is critical for the success of commercial search engines. Rank Boost algorithm is promising in Web relevance ranking area, while its computation complexity makes our existing implementations (including single node software-based implementation and a FPGA-based accelerator) too slow to reflect the dynamics of the...
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...
The increasing system resources available on field-programmable gate arrays (FPGA) enable the integration of complex system on one programmable chip. This paper focuses on the design and implementation of a hierarchy-bus based multi-processor system-on-chip (MPSoC) integrating 4 ARM processors on FPGA. Experimental results had been obtained running at 60MHz with total area requiring 34% adaptive look-up...
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