A reconfigurable computing architecture for semantic information filtering

• Published in: 2013 IEEE International Conference on Big Data pp. 212 - 218
• Main Authors: Tripathy, Aalap, Ka Chon Ieong, Patra, Atish, Mahapatra, Rabi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2013
• Subjects: Bloom Filter; Computer aided manufacturing; Computer architecture; GPGPU; information filtering; Program processors; Random access memory; recommendation systems; reconfigurable computing; SCC; semantic comparison; Semantics; System-on-chip; Tensile stress
• DOI: 10.1109/BigData.2013.6691577

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.