• DocumentCode
    3079403
  • Title

    A Deep Learning Prediction Process Accelerator Based FPGA

  • Author

    Qi Yu ; Chao Wang ; Xiang Ma ; Xi Li ; Xuehai Zhou

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Sci. & Technol. of China, Hefei, China
  • fYear
    2015
  • fDate
    4-7 May 2015
  • Firstpage
    1159
  • Lastpage
    1162
  • Abstract
    Recently, machine learning is widely used in applications and cloud services. And as the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. To give users better experience, high performance implementations of deep learning applications seem very important. As a common means to accelerate algorithms, FPGA has high performance, low power consumption, small size and other characteristics. So we use FPGA to design a deep learning accelerator, the accelerator focuses on the implementation of the prediction process, data access optimization and pipeline structure. Compared with Core 2 CPU 2.3GHz, our accelerator can achieve promising result.
  • Keywords
    field programmable gate arrays; learning (artificial intelligence); complex learning problems; data access optimization; deep learning accelerator design; deep learning prediction process accelerator-based FPGA; field-programmable gate array; machine learning; pipeline structure; prediction process implementation; Clocks; Computer architecture; Field programmable gate arrays; Hardware; Interpolation; Neural networks; Training; FPGA; accelerator; deep learning; prediction process;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/CCGrid.2015.114
  • Filename
    7152611