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Çarpmasiz Yapay Sinir Aʇi

Translated title of the contribution: Multiplication-free Neural Networks
  • Cem Emre Akbaş
  • , Alican Bozkurt
  • , A. Enis Çetin
  • , Rengul Çetin-Atalay
  • , Ayşegül Üner
  • Bilkent University
  • Middle East Technical University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

In this article, a multiplication-free artificial Neural Network (ANN) structure is proposed. Inner products between the input vectors and the ANN weights are implemented using a multiplication-free vector operator. Training of the new artificial neural network structure is carried out using the sign-LMS algorithm. Proposed ANN system can be used in applications requiring low-power usage or running on microprocessors that have limited processing power.

Translated title of the contributionMultiplication-free Neural Networks
Original languageTurkish
Title of host publication2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2416-2418
Number of pages3
ISBN (Electronic)9781467373869
DOIs
Publication statusPublished - 19 Jun 2015
Event2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Malatya, Turkey
Duration: 16 May 201519 May 2015

Publication series

Name2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings

Conference

Conference2015 23rd Signal Processing and Communications Applications Conference, SIU 2015
Country/TerritoryTurkey
CityMalatya
Period16/05/1519/05/15

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