Hardware Accelerator Systems for Artificial Intelligence and Machine Learning (Volume 122) (Advances in Computers, Volume 122)

417 Pages·2021·23.584 MB·English

by  Shiho Kim (editor), Ganesh Chandra Deka (editor)

VOLUMEONEHUNDRED ANDTWENTY TWO
A
DVANCES IN
COMPUTERS
Hardware Accelerator Systems
for Artificial Intelligence
and Machine Learning
This page intentionally left blank
VOLUMEONEHUNDRED ANDTWENTY TWO
A
DVANCES IN
COMPUTERS
Hardware Accelerator Systems
for Artificial Intelligence
and Machine Learning
Edited by
SHIHO KIM
School of Integrated Technology, Yonsei University,
Seoul, South Korea
GANESH CHANDRA DEKA
Ministry of Skill Development and Entrepreneurship,
New Delhi, India
AcademicPressisanimprintofElsevier
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates
525BStreet,Suite1650,SanDiego,CA92101,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
125LondonWall,London,EC2Y5AS,UnitedKingdom
Firstedition2021
Copyright©2021ElsevierInc.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronic
ormechanical,includingphotocopying,recording,oranyinformationstorageandretrievalsystem,
withoutpermissioninwritingfromthepublisher.Detailsonhowtoseekpermission,further
informationaboutthePublisher’spermissionspoliciesandourarrangementswithorganizationssuch
astheCopyrightClearanceCenterandtheCopyrightLicensingAgency,canbefoundatourwebsite:
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Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe
Publisher(otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience
broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical
treatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluating
andusinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuch
informationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,including
partiesforwhomtheyhaveaprofessionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assume
anyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability,
negligenceorotherwise,orfromanyuseoroperationofanymethods,products,instructions,orideas
containedinthematerialherein.
ISBN:978-0-12-823123-4
ISSN:0065-2458
ForinformationonallAcademicPresspublications
visitourwebsiteathttps://www.elsevier.com/books-and-journals
Publisher:ZoeKruze
DevelopmentalEditor:TaraA.Nadera
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TypesetbySPiGlobal,India
Contents
Contributors ix
Preface xi
1. Introduction to hardware acceleratorsystemsfor artificial
intelligence andmachine learning 1
NehaGupta
1. Introductiontoartificialintelligenceandmachinelearninginhardware
acceleration 2
2. Deeplearningandneuralnetworkacceleration 4
3. HWacceleratorsforartificialneuralnetworksandmachinelearning 8
4. SWframeworkfordeepneuralnetworks 13
5. ComparisonofFPGA,CPUandGPU 16
6. Conclusionandfuturescope 19
References 19
Abouttheauthor 21
2. Hardware acceleratorsystemsfor embedded systems 23
WilliamJ. Song
1. Introduction 24
2. Neuralnetworkcomputinginembeddedsystems 26
3. Hardwareaccelerationinembeddedsystems 34
4. Softwareframeworksforneuralnetworks 42
Acknowledgments 44
References 44
Abouttheauthor 49
3. Hardware acceleratorsystemsfor artificial intelligence
andmachine learning 51
HyunbinPark and ShihoKim
1. Introduction 52
2. Background 53
3. Hardwareinferenceacceleratorsfordeepneuralnetworks 66
4. Hardwareinferenceacceleratorsusingdigitalneurons 78
5. Summary 88
Acknowledgments 89
References 90
Abouttheauthors 94
v
vi Contents
4. Genericquantum hardwareacceleratorsfor conventional
systems 97
Parth Bir
1. Introduction 98
2. Principlesofcomputation 98
3. Needandfoundationforquantumhardwareacceleratordesign 100
4. Agenericquantumhardwareaccelerator(GQHA) 118
5. Industriallyavailablequantumhardwareaccelerators 125
6. Conclusionandfuturework 130
References 130
Abouttheauthor 133
5. FPGA based neural network accelerators 135
Joo-YoungKim
1. Introduction 136
2. Background 137
3. Algorithmicoptimization 142
4. Acceleratorarchitecture 147
5. Designmethodology 154
6. Applications 157
7. Evaluation 158
8. Futureresearchdirections 160
References 160
Abouttheauthor 164
6. Deep learningwith GPUs 167
WonJeon, GunKo, Jiwon Lee,Hyunwuk Lee,Dongho Ha,
and WonWooRo
1. DeeplearningapplicationsusingGPUasaccelerator 168
2. Overviewofgraphicsprocessingunit 171
3. DeeplearningaccelerationinGPUhardwareperspective 181
4. GPUsoftwareforacceleratingdeeplearning 188
5. AdvancedtechniquesforoptimizingdeeplearningmodelsonGPUs 196
6. ConsandprosofGPUaccelerators 207
Acknowledgment 208
References 209
Furtherreading/Referencesforadvance 213
Abouttheauthors 213
Contents vii
7. Architectureofneuralprocessingunitfordeepneuralnetworks 217
Kyuho J. Lee
1. Introduction 218
2. Background 219
3. Considerationsinhardwaredesign 222
4. NPUarchitectures 223
5. Discussion 235
6. Summary 238
Acknowledgments 239
References 239
Furtherreading 243
Abouttheauthor 245
8. Energy-efficient deep learning inference onedge devices 247
FrancescoDaghero, DanieleJahier Pagliari, and Massimo Poncino
1. Introduction 248
2. Theoreticalbackground 249
3. Deeplearningframeworksandlibraries 258
4. Advantagesofdeeplearningontheedge 259
5. Applicationsofdeeplearningattheedge 260
6. Hardwaresupportfordeeplearninginferenceattheedge 262
7. Staticoptimizationsfordeeplearninginferenceattheedge 265
8. Dynamic(input-dependent)optimizationsfordeeplearninginferenceat
theedge 282
9. Openchallengesandfuturedirections 293
References 293
Abouttheauthors 301
9. “Lastmile”optimization of edge computingecosystem with
deep learning models and specialized tensor processing
architectures 303
YuriGordienko, YuriyKochura, VladTaran, NikitaGordienko,
OleksandrRokovyi, OlegAlienin, and SergiiStirenko
1. Introduction 304
2. Stateoftheart 306
3. Methodology 311
4. Results 319
5. Discussion 330
viii Contents
6. Conclusions 332
Acknowledgments 333
References 333
Furtherreading 339
Abouttheauthors 339
10. Hardwareacceleratorfortrainingwithintegerbackpropagation
and probabilisticweight update 343
Hyunbin Parkand Shiho Kim
1. Introduction 344
2. Integerbackpropagationwithprobabilisticweightupdate 347
3. Considerationofhardwareimplementationoftheprobabilisticweight
update 354
4. Simulationresultsoftheproposedscheme 356
5. Discussions 359
6. Summary 361
Acknowledgments 361
References 362
Abouttheauthors 364
11. Musicrecommender system using restrictedBoltzmann
machine with implicit feedback 367
Amitabh Biswal,Malaya Dutta Borah,and ZakirHussain
1. Introduction 368
2. Typesofrecommendersystems 371
3. Problemstatement 386
4. ExplanationofRBM 386
5. Proposedarchitecture 390
6. Minibatchsizeusedfortrainingandselectionofweightsandbiases 395
7. Typesofactivationfunctionthatcanbeusedinthismodel 395
8. Evaluationmetricsthatcanbeusedtomeasureformusic
recommendation 396
9. Experimentalsetup 397
10. Result 398
11. Conclusion 399
12. Futureworks 399
Reference 399
Abouttheauthors 401
AcademicPressisanimprintofElsevier
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates
525BStreet,Suite1650,SanDiego,CA92101,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
125LondonWall,London,EC2Y5AS,UnitedKingdom
Firstedition2021
Copyright©2021ElsevierInc.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronic
ormechanical,includingphotocopying,recording,oranyinformationstorageandretrievalsystem,
withoutpermissioninwritingfromthepublisher.Detailsonhowtoseekpermission,further
informationaboutthePublisher’spermissionspoliciesandourarrangementswithorganizationssuch
astheCopyrightClearanceCenterandtheCopyrightLicensingAgency,canbefoundatourwebsite:
www.elsevier.com/permissions.
Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe
Publisher(otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience
broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical
treatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluating
andusinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuch
informationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,including
partiesforwhomtheyhaveaprofessionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assume
anyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability,
negligenceorotherwise,orfromanyuseoroperationofanymethods,products,instructions,orideas
containedinthematerialherein.
ISBN:978-0-12-823123-4
ISSN:0065-2458
ForinformationonallAcademicPresspublications
visitourwebsiteathttps://www.elsevier.com/books-and-journals
Publisher:ZoeKruze
DevelopmentalEditor:TaraA.Nadera
ProductionProjectManager:JamesSelvam
CoverDesigner:AlanStudholme
TypesetbySPiGlobal,India