Towards Efficient Hardware Acceleration of Deep Neural Networks on FPGA

104 Pages·2017·9.72 MB·English

by  Sicheng Li

TOWARDS EFFICIENT HARDWARE ACCELERATION OF
DEEP NEURAL NETWORKS ON FPGA
by
Sicheng Li
M.S. in Electrical Engineering,
New York University, 2013
B.S. in Electrical Engineering,
Beijing University of Posts and Communications, 2011
Submitted to the Graduate Faculty of
the Swanson School of Engineering in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
University of Pittsburgh
2017
UNIVERSITYOFPITTSBURGH
SWANSONSCHOOLOFENGINEERING
Thisdissertationwaspresented
by
SichengLi
Itwasdefendedon
September292017
andapprovedby
Hai(Helen)Li,Ph.D.,AdjunctAssociateProfessor,DepartmentofElectricalandComputer
Engineering
YiranChen,Ph.D.,AdjunctAssociateProfessor,DepartmentofElectricalandComputer
Engineering
Zhi-HongMao,Ph.D.,AssociateProfessor,DepartmentofElectricalandComputerEngineering
ErvinSejdic,Ph.D.,AssociateProfessor,DepartmentofElectricalandComputerEngineering
YuWang,Ph.D.,AssociateProfessor,DepartmentofElectricalEngineering,TsinghuaUniversity
DissertationDirector: Hai(Helen)Li,Ph.D.,AdjunctAssociateProfessor,Departmentof
ElectricalandComputerEngineering
ii
Copyright(cid:13)c bySichengLi
2017
iii
TOWARDS EFFICIENT HARDWARE ACCELERATION OF
DEEP NEURAL NETWORKS ON FPGA
SichengLi,PhD
UniversityofPittsburgh,2017
Deep neural network (DNN) has achieved remarkable success in many applications because of its
powerful capability for data processing. Their performance in computer vision have matched and
insomeareasevensurpassedhumancapabilities. Deepneuralnetworkscancapturecomplexnon-
linear features; however this ability comes at the cost of high computational and memory require-
ments. State-of-art networks require billions of arithmetic operations and millions of parameters.
The brute-force computing model of DNN often requires extremely large hardware resources, in-
troducing severe concerns on its scalability running on traditional von Neumann architecture. The
well-known memory wall, and latency brought by the long-range connectivity and communica-
tion of DNN severely constrain the computation efficiency of DNN. The acceleration techniques
of DNN, either software or hardware, often suffer from poor hardware execution efficiency of the
simplifiedmodel(software),orinevitableaccuracydegradationandlimitedsupportablealgorithms
(hardware), respectively. In order to preserve the inference accuracy and make the hardware im-
plementation in a more efficient form, a close investigation to the hardware/software co-design
methodologiesforDNNsisneeded.
The proposed work first presents an FPGA-based implementation framework for Recurrent
Neural Network (RNN) acceleration. At architectural level, we improve the parallelism of RNN
training scheme and reduce the computing resource requirement for computation efficiency en-
hancement. Thehardware implementation primarily targets at reducing datacommunication load.
Secondly,weproposeadatalocality-awaresparsematrixandvectormultiplication(SpMV)kernel.
At software level, we reorganize a large sparse matrix into many modest-sized blocks by adopt-
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ing hypergraph-based partitioning and clustering. Available hardware constraints have been taken
into consideration for the memory allocation and data access regularization. Thirdly, we present
a holistic acceleration to sparse convolutional neural network (CNN). During network training,
the data locality is regularized to ease the hardware mapping. The distributed architecture enables
high computation parallelism and data reuse. The proposed research results in an hardware/soft-
ware co-design methodology for fast and accurate DNN acceleration, through the innovations in
algorithm optimization, hardware implementation, and the interactive design process across these
twodomains.
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TABLEOFCONTENTS
1.0 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 DeepNeuralNetworksonFPGAs . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 DissertationContributionandOutline . . . . . . . . . . . . . . . . . . . . . . . . 2
2.0 FPGAACCELERATIONTORNN-BASEDLANGUAGEMODEL . . . . . . . . . 6
2.1 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 LanguageModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 RNN&RNNbasedLanguageModel . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 TheRNNTraining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 AnalysisforDesignOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 ArchitectureOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 IncreaseParallelismbetweenHiddenandOutputLayers . . . . . . . . . . . 11
2.3.2 ComputationEfficiencyEnhancement . . . . . . . . . . . . . . . . . . . . . 13
2.4 HardwareImplementationDetails . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.1 SystemOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.2 DataAllocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.3 ThreadManagementinComputationEngine . . . . . . . . . . . . . . . . . 17
2.4.4 ProcessingElementDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.5 DataReuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.1 ExperimentSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.2 TrainingAccuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5.3 SystemPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
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2.5.4 ComputationEngineEfficiency . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.0 THERECONFIGURABLESPMVKERNEL . . . . . . . . . . . . . . . . . . . . . 27
3.1 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.1 SparseMatrixPreprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.2 TheExistingSpMVArchitectures . . . . . . . . . . . . . . . . . . . . . . . 28
3.2 DesignFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 SparseMatrixClustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3.1 WorkloadBalance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3.2 Hardware-awareClustering . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.3 StrongScalingvs. WeakScaling . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.4 HardwareConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4 HardwareImplementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 GlobalControlUnit(GCU) . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.2 ProcessingElement(PE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.3 ThreadManagementUnit(TMU) . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.4 HardwareConfigurationOptimization . . . . . . . . . . . . . . . . . . . . . 41
3.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5.1 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5.2 SystemPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.3 TheImpactofDataPreprocessing . . . . . . . . . . . . . . . . . . . . . . . 46
3.5.4 ComparisontoPreviousDesigns . . . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.0 SPARSECONVOLUTIONALNEURALNETWORKSONFPGA . . . . . . . . . 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2 CNNAccelerationandDifficulty . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.1 DenseCNNAcceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.2 InefficientAccelerationofSparseCNN . . . . . . . . . . . . . . . . . . . . 54
4.3 TheProposedDesignFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.4 CNNModelSparsification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
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4.4.1 Locality-awareRegularization . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.2 SparseNetworkRepresentation . . . . . . . . . . . . . . . . . . . . . . . . 60
4.4.3 KernelCompressionandDistribution . . . . . . . . . . . . . . . . . . . . . 62
4.5 HardwareImplementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5.1 TheSystemArchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5.2 ThePEOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.5.3 ZeroSkippingforComputationEfficiency . . . . . . . . . . . . . . . . . . . 68
4.5.4 DataReusetoImproveEffectiveBandwidth . . . . . . . . . . . . . . . . . . 69
4.6 HardwareSpecificOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.6.1 DesignTrade-offsonCrossLayerSparsity . . . . . . . . . . . . . . . . . . 70
4.6.2 HardwareConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.7.1 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.7.2 Layer-by-LayerPerformance . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.7.3 End-to-EndSystemIntegration . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.0 RELATEDWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.0 CONCLUSIONANDFUTUREWORK . . . . . . . . . . . . . . . . . . . . . . . . 83
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
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LISTOFTABLES
1 RNNComputationRuntimeBreakdowninGPU . . . . . . . . . . . . . . . . . . . 10
2 MemoryAccessRequirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 ResourceUtilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4 AccuracyEvaluationonMSRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5 ConfigurationofDifferentPlatforms . . . . . . . . . . . . . . . . . . . . . . . . . 22
6 Runtime(inSeconds)ofRNNLMswithDifferentNetworkSize . . . . . . . . . . . 23
7 PowerConsumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8 ComputationEngineEfficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9 ResourceUtilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
10 CharacteristicsofBenchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
11 ConfigurationandSystemPerformanceofDifferentPlatforms . . . . . . . . . . . . 44
12 SystemPropertiesforPreviousImplementationsandThisWork . . . . . . . . . . . 48
13 The average weight sparsity and accuracy of three selected CNN models after reg-
ularization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
14 The average sparsity and replication rate of the input feature maps of Conv layers
inAlexNet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
15 Configurationofdifferentplatforms . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16 ResourceutilizationonFPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
17 PerformanceevaluationonsparseConvandFCLayersofAlexNetonImageNet . . 74
18 ComparisontopreviousFPGAworks . . . . . . . . . . . . . . . . . . . . . . . . . 77
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LISTOFFIGURES
1 (a)Feedforwardneuralnetwork;(b)Recurrentneuralnetwork. . . . . . . . . . . . 7
2 UnfoldRNNfortrainingthroughBPTTalgorithm. . . . . . . . . . . . . . . . . . . 9
3 The system speedup is saturated with the increment of CPU cores as the memory
accessesbecomethenewbottleneck. . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 The data flow of a two-stage pipelined RNN structure [1]. The computation com-
plexity of white boxes in output layer is much bigger than that of gray boxes in
hiddenlayer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 OurproposedparallelarchitectureforRNNLM. . . . . . . . . . . . . . . . . . . . 13
6 The impact of the reduced data precision of W in RNNLM on the reconstruction
ho
qualityofwordsequences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7 AnoverviewoftheRNNLMhardwareimplementation. . . . . . . . . . . . . . . . 15
8 Thethreadmanagementunitinacomputationengine. PEsareconnectedthrougha
crossbartothesharedmemorystructure. . . . . . . . . . . . . . . . . . . . . . . . 17
9 Themulti-threadbasedprocessingelement. . . . . . . . . . . . . . . . . . . . . . . 18
10 Anexampleofcompressedrowstorage(CSR). . . . . . . . . . . . . . . . . . . . . 28
11 ThesparsityaffectsSpMVperformance. . . . . . . . . . . . . . . . . . . . . . . . 29
12 Ourproposeddatalocality-awaredesignframeworkforSpMVacceleration. . . . . 31
13 Applyingthepartitioningandclusteringonasparsematrix(lns 3937). . . . . . . 33
14 Theperformancepenaltybroughtbyinter-PEcommunications. . . . . . . . . . . . 36
15 ThearchitectureforSpMVacceleration. . . . . . . . . . . . . . . . . . . . . . . . 38
16 Configurationheaderfileisusedtoscheduleinter-PEcommunication. . . . . . . . . 38
17 Theinternalstructureofprocessingelementdesign. . . . . . . . . . . . . . . . . . 40
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