Sigmoid generators for neural computing using piecewise approximations

doi:10.1109/12.537127

Sigmoid generators for neural computing using piecewise approximations

1996-01-01

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AI

DOI：10.1109/12.537127

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Abstract

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A piecewise second order approximation scheme is proposed for computing the sigmoid function. The scheme provides high performance with low implementation cost; thus, it is suitable for hardwired cost effective neural emulators. It is shown that an implementation of the sigmoid generator outperforms, in both precision and speed, existing schemes using a bit serial pipelined implementation. The proposed generator requires one multiplication, no look-up table and no addition. It has been estimated that the sigmoid output is generated with a maximum computation delay of 21 bit serial machine cycles representing a speedup of 1.57 to 2.23 over other proposals.

Keywords:

nonlinear function generators

sigmoid function

piecewise approximations

neural networks

hardware for twos complement notation

error analysis

Sigmoid generators for neural computing using piecewise approximations

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