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Cost-Performance Comparison of Various Accelerator Implementation Platforms for Deep Convolutional Neural Network

  • Yechan Yu
  • HoJin Kim
  • Jinjoo Ha
  • Daewoo Kim
  • Kang YiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)

Abstract

Due to the high accuracy, DCNN is a popular deep learning approach for object recognition and classification. But, the computing complexity of DCNN is too high for real-time application. Therefore, many acceleration methods like GPU and FPGAs are developed and competing with each other. The purpose of this paper is to assess the pros and the cons of many acceleration methods including GPGPU and FPGA-based approaches like Xilinx SDSoC, and Xilinx SDAccel. We will consider the installation cost (board price) as well as the operation cost (the energy consumption) and the speed of each acceleration method in the analysis.

Keywords

Reconfigurable high-performance computing Heterogeneous computing systems Intelligent computing and neural networks 

Notes

Acknowledgement

This work was supported by National program for Excellence in software at Handong Global University (2017-0-00130) funded by Ministry of Science and ICT in Korea.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yechan Yu
    • 1
  • HoJin Kim
    • 1
  • Jinjoo Ha
    • 1
  • Daewoo Kim
    • 1
  • Kang Yi
    • 1
    Email author
  1. 1.School of Computer Science and Electrical EngineeringHandong Global UniversityPohangRepublic of Korea

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