Content-based information retrieval using an embedded neural associative memory

Schmidt M, Rückert U. Content-based information retrieval using an embedded neural associative memory. In: Parallel and Distributed Processing, 2001. Proceedings. Ninth Euromicro Workshop on. IEEE Comput. Soc; 2001: 443-450.In this paper a novel approach for the storage and access of an index used in internet search engines (Information Retrieval) is presented. The index provides a mapping from search terms to documents. The Binary Neural Associative Memory (BiNAM) stores an index by associating document signatures and document locations in a distributed and content addressable way. The system presented here has a high memory efficiency of more than 90%. The trade-off between memory consumption and precision of the query-results is examined. A scalable system architecture is presented. The architecture exploits the parallel structure of the BiNAM. The association time is estimated to be orders of magnitude faster than a software solution. The system is realized as a modular PCI architecture. The maximum capacity of the first version is 768MByte memory which allows to implement a BiNAM of 80k neurons with 80k inputs each. In such a system approximately 64 million associations can be stored and accessed within 330ns per association