Controlled accuracy approximation of sigmoid function for efficient FPGA-based implementation of artificial neurons

A controlled accuracy approximation scheme of the sigmoid function for artificial neuron implementation based on Taylor's theorem and the Lagrange form of the error is proposed. The main advantages of the proposed solution are two: it provides a systematic way to guarantee the required accuracy...

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Bibliographic Details
Published in:Electronics letters 2013-12, Vol.49 (25), p.1598-1600
Main Authors: del Campo, I, Finker, R, Echanobe, J, Basterretxea, K
Format: Article
Language:English
Subjects:
Accuracy
Applied sciences
approximation theory
Artificial intelligence
artificial neural networks
Bioinspired technology
Computer science
control theory
systems
Connectionism. Neural networks
controlled accuracy approximation scheme
Exact sciences and technology
field programmable gate arrays
FPGA‐based artificial neuron implementation
Lagrange error form
learning (artificial intelligence)
linear neuron part circuitry
neural nets
online learning capabilities
sigmoid derivative
sigmoid function
Taylor theorem
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Summary:A controlled accuracy approximation scheme of the sigmoid function for artificial neuron implementation based on Taylor's theorem and the Lagrange form of the error is proposed. The main advantages of the proposed solution are two: it provides a systematic way to guarantee the required accuracy and it reuses the circuitry of the linear part of the neuron to compute the sigmoid function. The sigmoid derivative is also available for artificial neural networks with online learning capabilities.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2013.3098