Efficiency Optimization of Trainable Feature Extractors for a Consumer Platform

  • Maurice Peemen
  • Bart Mesman
  • Henk Corporaal
Conference paper

DOI: 10.1007/978-3-642-23687-7_27

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)
Cite this paper as:
Peemen M., Mesman B., Corporaal H. (2011) Efficiency Optimization of Trainable Feature Extractors for a Consumer Platform. In: Blanc-Talon J., Kleihorst R., Philips W., Popescu D., Scheunders P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg

Abstract

This paper proposes an algorithmic optimization for the feature extractors of biologically inspired Convolutional Neural Networks (CNNs). CNNs are successfully used for different visual pattern recognition applications such as OCR, face detection and object classification. These applications require complex networks exceeding 100,000 interconnected computational nodes. To reduce the computational complexity a modified algorithm is proposed; real benchmarks show 65 - 83% reduction, with equal or even better recognition accuracy. Exploiting the available parallelism in CNNs is essential to reduce the computational scaling problems. Therefore the modified version of the algorithm is implemented and evaluated on a GPU platform to demonstrate the suitability on a cost effective parallel platform. A speedup of 2.5x with respect to the standard algorithm is achieved.

Keywords

Convolutional Neural Networks Feature Extraction GPU 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maurice Peemen
    • 1
  • Bart Mesman
    • 1
  • Henk Corporaal
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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