An Efficient Hardware Implementation of Feed-Forward Neural Networks

Authors:
Tamás Szabó;Gábor Horváth
Affiliations:
-;-
Venue:
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Year:
2001

Citing 5
Cited 0

Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

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On monotone spline approximation

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Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results

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Fundamentals of Artificial Neural Networks

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Some new results on neural network approximation

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Abstract

This paper proposes a new way of digital hardware implementation of nonlinear activation functions in feed-forward neural networks. The basic idea of this new realization is that the nonlinear functions can be implemented using a matrix-vector multiplication. Recently a new approach was proposed for the realization of matrix-vector multiplications which approach can also be applied for implementing the nonlinear functions if the nonlinear functions are approximated by simple basis functions. The paper proposes to use B-spline basis functions to the approximate nonlinear sigmoidal functions, it shows that this approximation fulfills the general requirements on the activation functions, presents the details of the proposed hardware implementation, and gives a summary of an extensive study about the effects of B-spline nonlinear function realization on the size and the trainability of feed-forward neural networks.