Table Of ContentMemristive Devices for Brain-Inspired
Computing
From Materials, Devices, and Circuits to Applications —
Computational Memory, Deep Learning, and Spiking Neural
Networks
Woodhead Publishing Series in Electronic and
Optical Materials
Memristive Devices for
Brain-Inspired
Computing
From Materials, Devices, and Circuits to
Applications — Computational Memory,
Deep Learning, and Spiking Neural Networks
Edited by
Sabina Spiga
Abu Sebastian
Damien Querlioz
Bipin Rajendran
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Contents
Listofcontributors xv
Preface xix
Part I
(cid:1)
Memristive devices for brain inspired computing
1. Role of resistive memory devices in brain-inspired
computing 3
SabinaSpiga,AbuSebastian,DamienQuerliozandBipinRajendran
1.1 Introduction 3
1.2 Typeofresistivememorydevices 4
1.3 Resistivememorydevicesforbrain-inspiredcomputing 8
1.4 Conclusionsandperspectives 10
References 11
2. Resistive switching memories 17
StefanoBrivioandStephanMenzel
2.1 Introduction 17
2.2 Basicconceptsofthephysicsofresistiveswitching 17
2.2.1 Resistiveswitchingbasedoncationmigration 19
2.2.2 Resistiveswitchingbasedonanionmigration 21
2.2.3 Negativedifferentialresistancedevices 27
2.2.4 Switchingfeaturesrelatedtophysicalprocesses 28
2.3 Resistanceswitchingtechnology:performancesand
industrial-levelprototypes 32
2.4 Advancedfunctionalitiesandprogrammingschemes 35
2.4.1 Multileveloperation 36
2.4.2 Implementationofplasticityinresistiveswitching
randomaccessmemoriesdevices 38
2.4.3 Rateandtimingcomputingwithresistiveswitching
randomaccessmemoriesdevices 44
2.4.4 Oscillatorysystems 47
2.5 Conclusionsandperspectives 49
References 50
v
vi Contents
3. Phase-change memory 63
ManuelLeGalloandAbuSebastian
3.1 Introduction 63
3.1.1 Historicaloverviewofphase-changememory 63
3.1.2 Applicationsofphase-changememory 64
3.2 Essentialsofphase-changememory 67
3.3 Adetaileddescriptionofthewriteoperation 70
3.3.1 SET/RESEToperation 70
3.3.2 Switchingprocess 73
3.3.3 Multileveloperation 75
3.4 Adetaileddescriptionofthereadoperation 76
3.4.1 Subthresholdelectricaltransport:voltageand
temperaturedependence 77
3.4.2 Resistancedrift 78
3.4.3 Noise 81
3.5 Keyenablersforbrain-inspiredcomputing 82
3.5.1 Multilevelstorage 82
3.5.2 Accumulativebehavior 84
3.5.3 Interandintradevicerandomness 86
3.6 Outlook 90
References 91
4. Magnetic and ferroelectric memories 97
NicolasLocatelli,LizaHerreraDiezandThomasMikolajick
4.1 Magneticmemories 97
4.1.1 “Spintronics”ataglance 97
4.1.2 Storinginformation 98
4.1.3 Readinginformation 100
4.1.4 Writinginformation 105
4.1.5 Latestdevelopments 108
4.2 Ferroelectricmemories 109
4.2.1 Ferroelectricmaterials 109
4.2.2 Capacitor-basedferroelectricmemories 113
4.2.3 Transistor-basedferroelectricmemories 115
4.2.4 Ferroelectrictunnelingjunctions 116
4.3 MemoriesbeyondtheVonNeumannarchitectures 117
4.3.1 Logic-in-memory 118
4.3.2 Perspectivesforneuromorphiccomputing:
brain-inspiredarchitectures 120
4.3.3 Leveragingstochasticswitching:randomnumber
generation,approximatecomputing 125
4.3.4 Summaryandoutlook 126
References 127
Contents vii
5. Selector devices for emerging memories 135
SolomonAmsaluChekol,JeonghwanSong,JaehyukPark,
JongmyungYoo,SeokjaeLimandHyunsangHwang
5.1 Introduction 135
5.2 Insulator(cid:1)metaltransitionselector 137
5.3 Ovonicthresholdswitching 143
5.4 CBRAM-typeselector 149
5.5 Conclusion 157
References 160
Part II
Computational memory
6. Memristive devices as computational memory 167
AbuSebastian,DamienQuerlioz,BipinRajendranand
SabinaSpiga
6.1 Introduction 167
6.2 In-memorycomputing 167
6.3 Futureoutlook 171
References 172
7. Memristor-based in-memory logic and its application
in image processing 175
AmeerHaj-Ali,RonnyRonen,RotemBen-Hur,NimrodWaldand
ShaharKvatinsky
7.1 Introduction 175
7.2 Memristor-basedlogic 177
7.2.1 MemristorAidedloGIC(MAGIC) 180
7.2.2 Digitalimageprocessing 181
7.2.3 Previousattemptstoaccelerateimageprocessingwith
memristors 182
7.3 ThememristiveMemoryProcessingUnit 182
7.3.1 ChallengesofthememristiveMemoryProcessingUnit 184
7.4 PerformingimageprocessinginthememristiveMemory
ProcessingUnit 185
7.4.1 Fixed-Pointmultiplication 185
7.4.2 MAGIC-basedalgorithmsforimageprocessing 185
7.5 Evaluation 186
7.5.1 Methodology 186
7.5.2 Performance 188
7.5.3 Energy 188
7.6 Conclusions 189
References 190
viii Contents
8. Hyperdimensional computing nanosystem: in-memory
computing using monolithic 3D integration of RRAM
and CNFET 195
AbbasRahimi,TonyF.Wu,HaitongLi,JanM.Rabaey,
H.-S.PhilipWong,MaxM.ShulakerandSubhasishMitra
8.1 Introduction 195
8.2 BackgroundonHDcomputing 197
8.2.1 Arithmeticoperationsonhypervectors 198
8.2.2 Generalandscalablemodelofcomputing 200
8.2.3 Robustnessofcomputations 202
8.2.4 Memory-centricwithparalleloperations 202
8.3 Casestudy:languagerecognition 202
8.3.1 Mappingandencodingmodule 203
8.3.2 Similaritysearchmodule 205
8.4 EmergingtechnologiesforHDcomputing 206
8.4.1 Carbonnanotubefield-effecttransistors 206
8.4.2 ResistiveRAM 207
8.4.3 Monolithic3Dintegration 208
8.5 ExperimentaldemonstrationsforHDcomputing 209
8.5.1 3DVRRAMdemonstration:in-memoryMAPkernels 209
8.5.2 Systemdemonstrationusingmonolithic3Dintegrated
CNFETsandRRAM 212
8.6 Conclusion 215
References 215
9. Vector multiplications using memristive devices and
applications thereof 221
MohammedA.ZidanandWeiD.Lu
9.1 Introduction 221
9.2 Computingviaphysicallaws 223
9.2.1 Datamappingtothecrossbar 224
9.2.2 Inputdataencoding 226
9.2.3 Outputdatasampling 228
9.2.4 Additionaldesignconsiderations 230
9.3 Softcomputingapplications 230
9.3.1 Dataclassification 232
9.3.2 Featureextraction 236
9.3.3 Dataclustering 238
9.3.4 Signalprocessing 240
9.3.5 Securityapplications 242
9.4 Precisecomputingapplications 242
9.4.1 In-memoryarithmeticaccelerators 243
9.4.2 Logiccircuitry 245
9.5 Generalmemristor-basedmultiply-and-accumulate
accelerators 246
Contents ix
9.6 Conclusion 248
Acknowledgments 248
References 249
10. Computing with device dynamics 255
StephanieBohaichukandSuhasKumar
10.1 Computationusingoscillatorydynamics 256
10.2 Controlofmemristorresistance 262
10.3 Correlationdetectionandnonlinearsolvers 263
10.4 OptimizationusingHopfieldnetworksandchaoticdevices 267
10.5 Conclusions 270
References 271
11. Exploiting the stochasticity of memristive devices for
computing 275
AliceMizrahi,Raphae¨lLaurent,JulieGrollierandDamienQuerlioz
11.1 Harnessingrandomness 276
11.1.1 Trading-offreliabilityforlow-powerconsumption 276
11.1.2 Embracingunreliabilitybyusingnoise 277
11.1.3 Computingwithprobabilities:stochasticcomputing 285
11.2 Proposalsofstochasticbuildingblocks 287
11.2.1 Quantumdotscellularautomata 287
11.2.2 Molecularapproaches 288
11.2.3 Charge-basedmemristivedevices 289
11.2.4 Spintronics 292
11.3 Testcasesofstochasticcomputation:caseofmagnetic
tunneljunction 298
11.3.1 Spindice:atruerandomnumbergenerator 298
11.3.2 Stochasticsynapses 299
11.3.3 Stochasticcomputationwithsuperparamagnetic
tunneljunctions 301
11.3.4 Populationcoding-basedstochasticcomputation 302
11.4 Conclusion 303
References 304
Part III
Deep learning
12. Memristive devices for deep learning applications 313
DamienQuerlioz,SabinaSpiga,AbuSebastianandBipinRajendran
12.1 Quickintroductiontodeeplearning 314
x Contents
12.1.1 Simpleneuralnetwork 314
12.1.2 Backpropagation 316
12.1.3 Whygoingdeephelps? 318
12.1.4 Moderndeepneuralnetworks 319
12.2 Whydodeepneuralnetworksconsumemoreenergythan
thebrain,andhowmemristivedevicescanhelp 322
12.2.1 Separationoflogicandmemory 322
12.2.2 Relianceonapproximatecomputing 323
12.2.3 Costofclock 324
12.2.4 Isbackpropagationhardwarecompatible? 325
12.3 Conclusion 326
References 326
13. Analog acceleration of deep learning using
phase-change memory 329
PritishNarayanan,StefanoAmbrogio,HsinyuTsai,CharlesMackin,
RobertM.ShelbyandGeoffreyW.Burr
13.1 Introduction 329
13.2 Deeplearningwithnonvolatilememory—anoverview 331
13.3 Recentprogressonphase-changememoryfordeeplearning 336
13.4 Achievingsoftware-equivalentaccuracyinDNNtraining 341
13.4.1 PCM13T1C 342
13.4.2 Polarityinversion 344
13.4.3 Mixedhardware(cid:1)Softwareexperiment 346
13.4.4 Results 349
13.5 Nonvolatilememorydevicerequirementsfordeeplearning
revisited 352
13.5.1 Mostsignificantpairprogramming 354
13.5.2 Dependenceofaccuracyondevicenonidealities 354
13.6 Conclusions 359
References 359
14. RRAM-based coprocessors for deep learning 363
YingZhou,BinGao,ChunmengDou,Meng-FanChangand
HuaqiangWu
14.1 Introduction 363
14.2 NNapplicationsbasedonRRAM 367
14.2.1 Relatedsimulationwork 367
14.2.2 Experimentalimplementation 373
14.3 Circuitandsystem-levelimplementation 382
14.3.1 Latestprogressoncircuitandsystembasedon
RRAMforNNprocessing 382
