A Modularization Hardware Implementation Approach for Artificial Neural Network

DOI

10.2991/icecee-15.2015.134How to use a DOI?

Keywords

Artificial Neural Network; Modularization; Digitization; FPGA

Abstract

Hardware implementation has been proven to be an effective way to take full advantage of the parallel and distributed computation ability of artificial neural network. To simplify the hardware implementation process of different kinds of neural networks, a modularization and digitization implementation method based on FPGA is proposed. Firstly, some commonly used artificial neural network structures are divided into several functional modules, which are then digitized with HDL. Finally, the hardware implementation of an expected neural network can be achieved by combining those related modules with ease in FPGA. The modularization construction and hardware implementation process of a discrete Hopfield neural network is taken as an example to validate the feasibility and effectiveness of the method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tong Wang
AU  - Lianming Wang
PY  - 2015/06
DA  - 2015/06
TI  - A Modularization Hardware Implementation Approach for Artificial Neural Network
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 670
EP  - 675
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.134
DO  - 10.2991/icecee-15.2015.134
ID  - Wang2015/06
ER  -