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作者:

Lv, Ang (Lv, Ang.) | Wang, Chao (Wang, Chao.) | Hou, Ligang (Hou, Ligang.) | Zeng, Zhiyong (Zeng, Zhiyong.) | Guo, Jia (Guo, Jia.) | Jiang, Nan (Jiang, Nan.)

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EI Scopus

摘要:

In this paper, a floating point multiplication and accumulation operator based on FPGA is designed for neural network calculation, and a custom 32 bit floating-point data format is used to change the amount of computation by changing the overall structure of the data, and the performance of the operator is optimized. Finally, the simulation results in FPGA are given to verify the correctness of the design. The design saves the resources by comparing the floating point operation with the common algorithm of 32 bit floating-point data of the IEEE standard. © 2018 IEEE.

关键词:

Digital arithmetic Field programmable gate arrays (FPGA) IEEE Standards Integrated circuit design Integrated circuits Microsystems

作者机构:

  • [ 1 ] [Lv, Ang]Beijing University of Technology, College of Microelectronics, Beijing, China
  • [ 2 ] [Wang, Chao]Beijing University of Technology, College of Microelectronics, Beijing, China
  • [ 3 ] [Hou, Ligang]Beijing University of Technology, College of Microelectronics, Beijing, China
  • [ 4 ] [Zeng, Zhiyong]Beijing University of Technology, College of Microelectronics, Beijing, China
  • [ 5 ] [Guo, Jia]Beijing University of Technology, College of Microelectronics, Beijing, China
  • [ 6 ] [Jiang, Nan]Beijing University of Technology, College of Microelectronics, Beijing, China

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来源 :

年份: 2018

页码: 282-285

语种: 英文

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WoS核心集被引频次: 0

SCOPUS被引频次: 1

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