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