Image classification with energy efficient hadamard neural networks

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Author
Deveci, Tuba Ceren
Advisor
Çetin, A. Enis
Date
2018-01Publisher
Bilkent University
Language
English
Type
Thesis
Metadata
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http://hdl.handle.net/11693/35750Abstract
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.