Beta
ManageSearchChatDiscover
142391

Low-Voltage CMOS Circuits for Analog VLSI Programmable Neural Networks.

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Electronics and Communications Engineering

Abstract

This paper presents an overview of designing an analog VLSI system used for handwritten recognition; namely a programmable neural network in linear as well as subthreshold CMOS technology. Synaptic weights are designed in the triode region. In addition, the processing element (PE) and the Tanh, activation function are designed in subthreshold region. Such subthreshold CMOS technology has some interesting features, such as high integration density, exponential transfer characteristics and low-power consumption. The proposed system is realized in a standard 0.8um CMOS technology and operated with a ± 1V power supply. 

DOI

10.21608/bfemu.2021.142391

Authors

First Name

Mohy El-Din

Last Name

Abo El-Soud

MiddleName

-

Affiliation

Professor of Electronics & Communication Engineering Department., Faculty of Engineering., El- Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

First Name

Hassan

Last Name

Soliman

MiddleName

H.

Affiliation

Electronics & Communication Engineering Department., Faculty of Engineering.,El- Mansoura University., Mansoura., Egypt.

Email

hsoliman@mans.edu.eg

City

Mansoura

Orcid

-

First Name

Roshdy

Last Name

AbdelRassoul

MiddleName

A.

Affiliation

Senior Members IEEE., .Arabic Academy for Science & Technology and Maritime Transport, Alexandria.

Email

-

City

Alexandria

Orcid

-

First Name

Laila

Last Name

El-ghanam

MiddleName

M.

Affiliation

Electronics & Communication Engineering Department., Faculty of Engineering.,El- Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

Volume

28

Article Issue

4

Related Issue

20838

Issue Date

2003-12-01

Receive Date

2003-10-19

Publish Date

2021-01-23

Page Start

21

Page End

30

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 28_Issue 4_Pages 21-30.pdf

PDF . 230.4kB

Link

https://bfemu.journals.ekb.eg/article_142391.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=142391

Order

5

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023