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Implementation and Research of LSTM Neural Network Based on the FPGA

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DOI: 10.23977/jeis.2017.22003 | Downloads: 40 | Views: 4082

Author(s)

Xintao Huang 1, Jun Yang 1

Affiliation(s)

1 School of Information Science and Engineering, Yunnan university, Kunming, China

Corresponding Author

Jun Yang

ABSTRACT

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.

KEYWORDS

RNN, LSTM, FPGA.

CITE THIS PAPER

Xintao, H. , Jun, Y. (2017) Implementation and Research of LSTM Neural Network Based on the FPGA. Journal of Electronics and Information Science (2017) 2: 76-79.

REFERENCES

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[3] Wikipedia.(2015).Field-programmable gate array [Online]. 
[4] G. Orchard, J. G. Martin, R. J. Vogelstein, and R. Etienne-Cummings, “Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations,” vol. 24, no. 8, pp. 1239–1252, 2015.
[5] S. Chikkerur. (2008). CUDA Implementation of a Biologically Inspired Object Recognition System [Online].

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