Mlp neural network and on-line backpropagation learning implementation in a low-cost fpga

Authors:
Ernesto Ordoñez-Cardenas;Rene de J. Romero-Troncoso
Affiliations:
Universidad Autonoma de Queretaro, Queretaro, Mexico;Universidad de Guanajuato, Salamanca, Mexico
Venue:
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Year:
2008

Citing 4
Cited 1

Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation

ISSS '00 Proceedings of the 13th international symposium on System synthesis
FPGA-based real-time remote monitoring system

Computers and Electronics in Agriculture
The Impact of Arithmetic Representation on Implementing MLP-BP on FPGAs: A Study

IEEE Transactions on Neural Networks
Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization

IEEE Transactions on Neural Networks

Towards the designing of a robust intrusion detection system through an optimized advancement of neural networks

AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an implementation of a multilayer perceptronneural network and the backpropagation learning algorithm in an FPGA. The resulting implementation, in contrast to others, is a low-cost system with effective resource utilization, capable of training the neural network for any given task. The system is based on a modular scheme conforming to a system-on-a-chip (SoC), where modules can be replaced or scaled to suit a specific application. The system uses fixed-point arithmetic and it was carried out using generic hardware description language. A pipeline architecture is used in order to build a time-efficient system. The efficacy of the systems was tested in a pattern recognition application, tests were done in a low-cost Xilinx Spartan-3E FPGA.