A digital `snake´ implementation of the back-propagation neural network

Author

Piazza, F. ; Marchesi, M. ; Orlandi, G.

Author_Institution

Dept. of Electron. & Autom., Ancona Univ., Italy

fYear

1989

Firstpage

2185

Abstract

A digital implementation of a multilayer neural network model that has backpropagation as its learning algorithm is presented. This architecture is characterized by a set of elementary processors (neurons) and has the form of a linear sequence, where every processor communicates only with its two nearest neighbors. A sophisticated control of data exchange among neurons, by means of two data buses, ensures full pipelining in the forward mode. The proposed architecture is very flexible since, having only local connections, it can be easily expanded by simply adjoining more processors to it. Moreover, it can be programmed in terms of number and width of layers

Keywords

computer architecture; neural nets; pipeline processing; architecture; back-propagation neural network; backpropagation; data buses; data exchange; digital implementation; forward mode; full pipelining; learning algorithm; linear sequence; local connections; multilayer neural network model; neurons; snake implementation; Artificial neural networks; Data buses; Multi-layer neural network; Nearest neighbor searches; Neural networks; Neurons; Pipeline processing; Signal processing algorithms; Vectors; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1989., IEEE International Symposium on

Conference_Location

Portland, OR

Type

conf

DOI

10.1109/ISCAS.1989.100810

Filename

100810