A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

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...

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Vydáno v:IEEE transactions on computers Ročník 62; číslo 5; s. 886 - 899
Hlavní autoři: Qinru Qiu, Qing Wu, Bishop, M., Pino, R. E., Linderman, R. W.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.05.2013
Témata:
Biological neural networks
Brain models
Computational modeling
Computer architecture
distributed architecture
Heterogeneous (hybrid) systems
machine learning
natural language interfaces
Neurons
Optical character recognition software
ISSN:0018-9340
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Shrnutí: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.
ISSN:0018-9340
DOI:10.1109/TC.2012.50