Advanced Concepts for Intelligent Vision Systems

Volume 6915 of the series Lecture Notes in Computer Science pp 293-304

Efficiency Optimization of Trainable Feature Extractors for a Consumer Platform

  • Maurice PeemenAffiliated withEindhoven University of Technology
  • , Bart MesmanAffiliated withEindhoven University of Technology
  • , Henk CorporaalAffiliated withEindhoven University of Technology

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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.


Convolutional Neural Networks Feature Extraction GPU