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

Author

Gafsi, Z. ; Besbes, Khaoula

fYear

2001

fDate

29-31 Oct. 2001

Firstpage

189

Lastpage

192

Abstract

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 0.35μm under a frequency of 300 MHz shows the efficiency of the implementation.

Keywords

CMOS digital integrated circuits; floating point arithmetic; integrated circuit design; neural chips; polynomial approximation; 0.35 micron; 300 MHz; CMOS technology; activation function; artificial neural networks; digital hardware implementation; equivalent digital circuit; floating-point representation; polynomial approximation; sigmoid function; Artificial neural networks; CMOS technology; Chebyshev approximation; Circuit simulation; Digital circuits; Frequency; Neural network hardware; Neurons; Polynomials; Silicon;

fLanguage

English

Publisher

ieee

Conference_Titel

Microelectronics, 2001. ICM 2001 Proceedings. The 13th International Conference on

Print_ISBN

0-7803-7522-X

Type

conf

DOI

10.1109/ICM.2001.997519

Filename

997519

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2445107