Staff View
Structured deep neural network with low complexity

Descriptive

TitleInfo
Title
Structured deep neural network with low complexity
Name (type = personal)
NamePart (type = family)
Liao
NamePart (type = given)
Siyu
NamePart (type = date)
1993-
DisplayForm
Siyu Liao
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Yuan
NamePart (type = given)
Bo
DisplayForm
Bo Yuan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
Genre (authority = ExL-Esploro)
ETD doctoral
OriginInfo
DateCreated (qualifier = exact); (encoding = w3cdtf); (keyDate = yes)
2020
DateOther (type = degree); (qualifier = exact); (encoding = w3cdtf)
2020-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Deep Neural Network (DNN) has achieved great success in many fields. However, many DNN models are both deep and large thereby causing high storage and energy consumption during the training and inference phases. As the size of DNNs continues to grow, it is critical to improve computation efficiency and energy consumption while maintaining the corresponding model performance. Various methods have been proposed for compressing DNN models, which can be categorized into three different levels, model level, structure level, and weight level. This thesis focuses on structure enforcing compression algorithm and embedding quantization method which aims at:i)less storage and computation complexity, ii)easier hardware implementation because of structured memory access pattern, iii)natural language processing oriented embedding binarization. The first chapter introduces the motivation of this dissertation in detail. Chapter 2 goes over the background and the related work about compressing deep neural network. Chapter 3, Chapter 4 and Chapter 5 presents proposed compression methods for fully connected layer, convolution layer and embedding layer. Final chapter 6 discusses possible future directions of this research.
Subject (authority = local)
Topic
Deep learning
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_11156
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 80 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-m5vq-c879
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Liao
GivenName
Siyu
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-09-16 20:29:24
AssociatedEntity
Name
Siyu Liao
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-09-22T18:35:23
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-09-22T18:35:23
ApplicationName
pdfTeX-1.40.20
Back to the top
Version 8.5.5
Rutgers University Libraries - Copyright ©2024