Microsoft to accelerate Bing search with neural network

When we search Google’s web index, we are only searching around 10 percent of the half-a-trillion or so pages that are potentially available. Much of the content in the larger deep web — not to be confused with the dark web — is buried further down in the sites that make up the visible surface web. The indexes of competitors like Yahoo and Bing (around 15 billion pages each) are still only half as large as Google’s. To close this gap, Microsoft has recently pioneered sophisticated new Field-Programmable Gate Array (FPGA) technology to make massive web crawls more efficient, and faster.
 
Google’s engineers have previously estimated that a typical 0.2-second web query reflects a quantity of work spent in indexing and retrieval equal to about 0.0003 kWh of energy per search. With over 100 billion looks per month at their petabyte index, well-executed page ranking has become a formidable proposition. Microsoft’s approach with Bing has been to break the ranking portion of search into three parts — feature extraction, free-form expressions, and machine learning scoring:
http://www.extremetech.com/wp-content/uploads/2015/02/FPGAworkflow.jpg
 
 
Full Article