Digital hardware implementation of sigmoid function and its derivative for artificial neural networks

In this paper we propose a polynomial approximation of the sigmoid activation function and its derivative used in artificial neural networks, and we describe the design of the equivalent digital circuit using a floating-point representation for numbers. The simulation of the circuit realized with CM...

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Bibliographic Details
Published inICM 2001 Proceedings. The 13th International Conference on Microelectronics pp. 189 - 192
Main Authors Faiedh, H., Gafsi, Z., Besbes, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Artificial neural networks
Chebyshev approximation
Circuit simulation
CMOS technology
Digital circuits
Frequency
Neural network hardware
Neurons
Polynomials
Silicon
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Summary:In this paper we propose a polynomial approximation of the sigmoid activation function and its derivative used in artificial neural networks, and we describe the design of the equivalent digital circuit using a floating-point representation for numbers. The simulation of the circuit realized with CMOS technology AMS 0.35/spl mu/m under a frequency of 300 MHz shows the efficiency of the implementation.
ISBN:9780780375222
078037522X
DOI:10.1109/ICM.2001.997519