Education, Science, Technology, Innovation and Life
Open Access
Sign In

Implementation and Research of LSTM Neural Network Based on the FPGA

Download as PDF

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


Xintao Huang 1, Jun Yang 1


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

Corresponding Author

Jun Yang


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.




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.


[1] Y. Bengio, Learning Deep Architectures for AI, vol. 2, no. 1. 2009.
[2] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
[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].

Downloads: 8276
Visits: 279535

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.