Open AccessControlled accuracy approximation of sigmoid function for efficient FPGA‐based implementation of artificial neurons
Author(s) -
Campo I.,
Finker R.,
Echanobe J.,
Basterretxea K.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.3098
Subject(s) - sigmoid function , artificial neuron , computer science , artificial neural network , function (biology) , scheme (mathematics) , function approximation , field programmable gate array , taylor series , activation function , error function , algorithm , artificial intelligence , mathematics , mathematical analysis , computer hardware , evolutionary biology , biology
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.