面向卷积神经网络的 FPGA 设计

Translated title of the contribution: Accelerating convolutional neural networks on FPGAs

Liqiang Lu, Size Zheng, Qingcheng Xiao, Deming Chen, Yun Liang

Research output: Contribution to journalReview articlepeer-review

Abstract

In recent years, convolutional neural networks (CNNs) have become widely adopted for computer vision tasks. FPGAs have been adequately explored as a promising hardware accelerator for CNNs owing to their high performance, energy efficiency, and reconfigurability. However, previous FPGA methods, which are based on the conventional convolutional algorithm, are often bounded by the computational capability of FPGAs. This paper first introduces four convolution algorithms: 6-loop algorithm, general matrix-matrix multiplication (GEMM), Winograd algorithm, and fast Fourier transform (FFT) algorithm. Then, we present the implementations of these algorithms at home and abroad, and also introduce their corresponding optimization techniques.

Translated title of the contributionAccelerating convolutional neural networks on FPGAs
Original languageChinese (Traditional)
Pages (from-to)277-294
Number of pages18
JournalScientia Sinica Informationis
Volume49
Issue number3
DOIs
StatePublished - 2019

Keywords

  • CNN
  • convolution algorithm
  • fast algorithm
  • FFT
  • FPGA
  • Winograd

ASJC Scopus subject areas

  • General Computer Science
  • Engineering (miscellaneous)

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