A Reconfigurable Computing Architecture for Semantic Information Filtering
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The increasing amount of information accessible to a user digitally makes information retrieval & filtering difficult, time consuming and ineffective. New meaning representation techniques proposed in literature help to improve accuracy but increase problem size exponentially. In this paper, we present a novel reconfigurable computing architecture that addresses this issue, outperforms contemporary many-core processors such as Intel's Single Chip Cloud computer and Nvidia's GPU's by ∼20x for semantic information filtering. We validate our design using industry standard System-on-chip virtual prototyping and synthesis tools. Such a high performance reconfigurable architecture can form a template for a wide range of content-based and collaborative filtering engines used for big-data analytics. © 2013 IEEE.
author list (cited authors)
Tripathy, A., Ieong, K. C., Patra, A., & Mahapatra, R.