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

被引：33

作者：

del Campo, I. ^{[1
]}

Finker, R. ^{[1
]}

Echanobe, J. ^{[1
]}

Basterretxea, K. ^{[2
]}

机构：

[1] Univ Basque Country, Fac Sci & Technol, Dept Elect & Elect, Leioa 48940, Vizcaya, Spain

[2] Univ Basque Country, Tech Ind Engn Sch Bilbao, Dept Elect Technol, Bilbao 48940, Vizcaya, Spain

来源：

ELECTRONICS LETTERS | 2013年 / 49卷 / 25期

关键词：

HARDWARE;

D O I：

10.1049/el.2013.3098

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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

引用

页码：1598 / 1600

页数：2

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