• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
      • View Item
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Image classification with energy efficient hadamard neural networks

      Thumbnail
      View/Open
      Full printable version (1.387Mb)
      Author
      Deveci, Tuba Ceren
      Advisor
      Çetin, A. Enis
      Date
      2018-01
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Metadata
      Show full item record
      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/35750
      Abstract
      Deep learning has made significant improvements at many image processing tasks in recent years, such as image classification, object recognition and object detection. Convolutional neural networks (CNN), which is a popular deep learning architecture designed to process data in multiple array form, show great success to almost all detection & recognition problems and computer vision tasks. However, the number of parameters in a CNN is too high such that the computers require more energy and larger memory size. In order to solve this problem, we investigate the energy efficient network models based on CNN architecture. In addition to previously studied energy efficient models such as Binary Weight Network (BWN), we introduce novel energy efficient models. Hadamard-transformed Image Network (HIN) is a variation of BWN, but uses compressed Hadamardtransformed images as input. Binary Weight and Hadamard-transformed Image Network (BWHIN) is developed by combining BWN and HIN as a new energy ef- ficient model. Performances of the neural networks with di erent parameters and di erent CNN architectures are compared and analyzed on MNIST and CIFAR-10 datasets. It is observed that energy efficiency is achieved with a slight sacrifice at classification accuracy. Among all energy efficient networks, our novel ensemble model outperforms other energy efficient models.
      Embargo Lift Date
      2021-01-03
      Collections
      • Dept. of Electrical and Electronics Engineering - Master's degree 541

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the webmaster. Phone: (312) 290 1771
      Copyright © Bilkent University | Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin