NDLI: FPGA Implementation of a Pipelined On-Line Backpropagation

Content Provider	Springer Nature Link
Author	Gironés, Rafael Gadea Gironés, Rafael Gadea Palero, Ricardo Colom Boluda, Joaquín Cerdá Boluda, Joaquín Cerdá Cortés, Angel Sebastia
Copyright Year	2005
Abstract	The paper describes the implementation of a systolic array for a multilayer perceptron with a hardware-friendly learning algorithm. A pipelined modification of the on-line backpropagation algorithm is shown and explained. It better exploits the parallelism because both the forward and backward phases can be performed simultaneously. The neural network performance for the proposed modification is discussed and compared with the standard so-called on-line backpropagation algorithm in typical databases and with the various precisions required. Although the preliminary results are positive, subsequent theoretical analysis and further experiments with different training sets will be necessary. For this reason our VLSI systolic architecture—together with the combination of FPGA reconfiguration properties and a design flow based on generic VHDL—can create a reusable, flexible, and fast method of designing a complete ANN on a single FPGA and can permit very fast hardware verifications for our trials of the Pipeline On-line Backpropagation algorithm and the standard algorithms.
Starting Page	189
Ending Page	213
Page Count	25
File Format	PDF
ISSN	09225773
Journal	Journal of Signal Processing Systems
Volume Number	40
Issue Number	2
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-06-01
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	FPGA implementation Artificial Neural Networks (ANN) backpropagation algorithm VLSI systolic architecture Signal, Image and Speech Processing Circuits and Systems Electrical Engineering Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Information Systems Electrical and Electronic Engineering

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