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Heterogeneous Computing System for Deep Learning

  • Mihaela Maliţa
  • George Vlǎduţ Popescu
  • Gheorghe M. ŞtefanEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 866)

Abstract

Various forms of Deep Neural Network (DNN)  architectures are used as Deep Learning  tools for neural inspired computational systems. The computational power, the bandwidth and the energy requested by the current developments of the domain are very high. The solutions offered by the current architectural environment are far from being efficient. We propose a hybrid computational system for running efficiently the training and inference DNN algorithms. The system is more energy efficient compared with the current solutions, and achieves a higher actual performance per peak performance ratio. The accelerator part of our heterogeneous system is a programmable many-core system with a Map-Scan/Reductive only the cells where architecture. The chapter describes and evaluates the proposed accelerator for the main computational intensive components of a DNN: the fully connected layer, the convolution layer, the pooling layer, and the softmax layer.

Keywords

Deep neural network Parallel computing Heterogeneous computing Accelerators 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mihaela Maliţa
    • 1
  • George Vlǎduţ Popescu
    • 2
  • Gheorghe M. Ştefan
    • 2
    Email author
  1. 1.Saint Anselm CollegeManchesterUSA
  2. 2.Politehnica University of BucharestBucharestRomania

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