Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation Function

Meirambek Mukhametkhan, Olga Krestinskaya, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The implementation of analog neural network and online analog learning circuits based on memristive crossbar has been intensively explored in the recent years. The design of various activation functions is important for neuromorphic circuits and systems, especially deep leaning neural networks. There are several implementations of sigmoid and tangent activation function, while the analog hardware implementation of the neural networks with linear activation functions is an open problem. Therefore, this paper introduces a multilayer perceptron design with linear activation function using TSMC 130 μ mCMOS technology. In this paper, the performance of the proposed linear activation function is illustrated. In addition, the temperature variation and noise analysis are shown.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages245-249
Number of pages5
ISBN (Electronic)9781538659281
DOIs
Publication statusPublished - Sep 28 2018
Event2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan
Duration: Aug 15 2018Aug 17 2018

Conference

Conference2nd International Conference on Computing and Network Communications, CoCoNet 2018
CountryKazakhstan
CityAstana
Period8/15/188/17/18

Keywords

  • Crossbar
  • Linear Activation Function
  • Memristor
  • Multilayer perceptron

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation Function'. Together they form a unique fingerprint.

Cite this