A parallel neuromorphic text recognition system and its implementation on a heterogeneous high-performance computing cluster

Qinru Qiu, Qing Wu, Morgan Bishop, Robinson E. Pino, Richard W. Linderman

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

Given the recent progress in the evolution of high-performance computing (HPC) technologies, the research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition system (ITRS) that serves as the physical layer of machine reading. A parallel computing architecture is adopted that incorporates the HPC technologies with advances in neuromorphic computing models. The algorithm learns from what has been read and, based on the obtained knowledge, it forms anticipations of the word and sentence level context. The information processing flow of the ITRS imitates the function of the neocortex system. It incorporates large number of simple pattern detection modules with advanced information association layer to achieve perception and recognition. Such architecture provides robust performance to images with large noise. The implemented ITRS software is able to process about 16 to 20 scanned pages per second on the 500 trillion floating point operations per second (TFLOPS) Air Force Research Laboratory (AFRL)/Information Directorate (RI) Condor HPC after performance optimization.

Original languageEnglish (US)
Article number6158636
Pages (from-to)886-899
Number of pages14
JournalIEEE Transactions on Computers
Volume62
Issue number5
DOIs
StatePublished - Apr 10 2013

Keywords

  • Heterogeneous (hybrid) systems
  • distributed architecture
  • machine learning
  • natural language interfaces

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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