Implementation and Research of LSTM Neural Network Based on the FPGA
作者: Xintao HuangJun Yang
刊名: Journal of Electronics and Information Science, 2017, Vol.2 (2)
来源数据库: Clausius Scientific Press
DOI: 10.23977/jeis.2017.22003
原始语种摘要: Over the past decade, artificial intelligence has reached a stage of rapid development, and deep learning has played a main role in this development. Despite of its strong ability to simulate and predict, deep learning is faced with the problem of large computational complexity. At the hardware level, GPU, ASIC, FPGA are ways to solve the huge amount of computing. This paper will explain the deep learning, FPGA structure and the reason why the use of FPGA to accelerate the deep learning is effective. Also, it will introduce a recursive neural network (RNN) implementation on the FPGA platform.
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  • FPGA field programmable gate array
  • learning 学识
  • platform 台地
  • computational 计算的
  • intelligence 智能
  • Network 网络
  • simulate 模拟
  • artificial 人为的
  • development 开发
  • solve