各种光网络解决方案 相关技术的研究和探索Word下载.docx

各种光网络解决方案 相关技术的研究和探索Word下载.docx

《各种光网络解决方案 相关技术的研究和探索Word下载.docx》由会员分享，可在线阅读，更多相关《各种光网络解决方案 相关技术的研究和探索Word下载.docx（9页珍藏版）》请在冰豆网上搜索。

GPeterNet,agraphtheoreticframework,andFraNtiC,afractalgeometricarchitecture,forarbitraryaccessnetworkdeployments.Theperformanceofthesetopologiesisanalyzedintermsofdifferentsystemmetrics–topologicalrobustnessandreliability,systemcostsandnetworkexposureduetofailureconditions.Ouranalysisshowsthatacombinationofdifferentmesh-basedmulti-hopaccesstopologies,coupledwithemergingwirelessbackhaultechnologies,cancatercarrier-classservicesfornextgenerationradioaccessnetworks,providingsignificantadvantagesoverexistingaccesstechnologies.

ArticleOutline

1.Introduction

1.1.Motivationandpreviouswork

1.2.Ourcontributions

2.Opticalwirelesstechnology

3.ThePeterNetandGPeterNetarchitectures

3.1.ThegeneralizedPeterNet

4.TheFraNtiCarchitecture

4.1.Flexibilityandscalability

5.Robustness,reliabilityandnetworkexposure

5.1.Robustness

5.1.1.Centralityanditsroleinaccesstopology

5.2.Reliabilityanalysis

5.2.1.ReliabilityanalysisofFraNtiC

5.2.2.ReliabilityanalysisofGPeterNet

5.3.Networkexposure

6.Performanceevaluationframework

6.1.Systemparameters

6.2.Evaluationplatform

7.Conclusion

Acknowledgements

AppendixA. 

AppendixB. 

References

Vitae

硬件人工智能系统二十年的发展历程及启示和经验

Artificialneuralnetworksinhardware:

Asurveyoftwodecadesofprogress

Neurocomputing神经网络计算学报

Thisarticlepresentsacomprehensiveoverviewofthehardwarerealizationsofartificialneuralnetwork（ANN）models,knownashardwareneuralnetworks（HNN）,appearinginacademicstudiesasprototypesaswellasincommercialuse.HNNresearchhaswitnessedasteadyprogressformorethanlasttwodecades,thoughcommercialadoptionofthetechnologyhasbeenrelativelyslower.WestudytheoverallprogressinthefieldacrossallmajorANNmodels,hardwaredesignapproaches,andapplications.WeoutlineunderlyingdesignapproachesformappinganANNmodelontoacompact,reliable,andenergyefficienthardwareentailingcomputationandcommunicationandsurveyawiderangeofillustrativeexamples.Chipdesignapproaches（digital,analog,hybrid,andFPGAbased）atneuronallevelandasneurochipsrealizingcompleteANNmodelsarestudied.Wespecificallydiscuss,indetail,neuromorphicdesignsincludingspikingneuralnetworkhardware,cellularneuralnetworkimplementations,reconfigurableFPGAbasedimplementations,inparticular,forstochasticANNmodels,andopticalimplementations.Paralleldigitalimplementationsemployingbit-slice,systolic,andSIMDarchitectures,implementationsforassociativeneuralmemories,andRAMbasedimplementationsarealsooutlined.Wetracetherecenttrendsandexplorepotentialfutureresearchdirections.

1.Introduction

2.Evaluationparametersandclassification

2.1.Hardwareneuralnetworkclassification

3.Hardwareapproachestoneuronaldesign

3.1.Digitalneuron

3.2.Analogneuron

3.3.Siliconimplementationofspikingneuronanditssynapticdynamics

4.Hardwareneuralnetworkchips

4.1.Digitalneurochips

4.2.Analogneurochips

4.3.Hybridneurochips

4.4.FPGAbasedimplementations

4.5.Otherimplementations

4.5.1.Associativeneuralmemories

4.5.2.RAMbasedimplementations

5.CNNimplementations

6.NeuromorphicHNNs

6.1.Spikingneuralnetworkhardware

7.Opticalneuralnetworks

8.Conclusionsanddiscussion

Databasearchitectures:

Currenttrendsandtheirrelationshipstoenvironmentaldatamanagement 

数据结构：

当前发展趋势及其与数据管理环境的关系

EnvironmentalModelling&

Software

AnMPLS-basedarchitectureforscalableQoSandtrafficengineeringinconvergedmultiservicemobileIPnetworks 

Impactofnetworkstructureonthecapacityofwirelessmultihopadhoccommunication 

PhysicaA:

StatisticalMechanicsanditsApplication

网络结构设置与网络通信能力的关系及相互影响无线网络通讯多路由连接与布局

Reconfigurableturbodecodingfor3Gapplications 

SignalProcessing

3G应用软件中，本蓝C语言代码调试与程序编写

Softwareradioandreconfigurablesystemsrepresentreconfigurablefunctionalitiesoftheradiointerface.Consideringturbodecodingfunctioninbattery-powereddeviceslike3GPPmobileterminals,itwouldbedesirabletochoosetheoptimumdecodingalgorithm:

SOVAintermsoflatency,andlog-MAPintermsofperformance.Inthispaperitisshownthatthetwoalgorithmssharecommonoperations,makingfeasibleareconfigurableSOVA/log-MAPturbodecoderwithincreasedefficiency.Moreover,animprovementintheperformanceofthereconfigurablearchitectureisalsopossibleatminimumcost,byscalingtheextrinsicinformationwithacommonfactor.Theimplementationoftheimprovedreconfigurabledecoderwithinthe3GPPstandardisalsodiscussed,consideringdifferentscenarios.Ineachscenariovariousframelengthsareevaluated,whilethefourpossibleserviceclassesareapplied.InthecaseofAWGNchannels,theoptimumalgorithmisproposedaccordingtothedesiredqualityofserviceofeachclass,whichisdeterminedfromlatencyandperformanceconstraints.Ouranalysisshowsthepotentialutilityofthereconfigurabledecoder,sincethereisanoptimumalgorithmformostscenarios.

2.WhyreconfigurationonlybetweenSOVAandlog-MAP?

3.Mathematicalanalysisofthealgorithms

3.1.SOVAanalysis

3.2.Log-MAPanalysis

3.3.ReconfigurableoperationsbetweenSOVAandlog-MAP

3.3.1.BMCblock

3.3.2.FSMCandRSMCblocks

3.3.2.1.FPMC,RPMCsub-blocks

3.3.2.2.FAC,RACsub-blocks

3.3.2.3.SPC,FACsub-blocks

4.Datatransferin3GPP

4.1.Qualityofservicearchitecture

5.Simulationmodel

6.ImprovingtheSOVA/log-MAPreconfigurabledecoder

7.Latencycalculation

8.Simulationresultsandimplementationscenariosin3GPP

8.1.Scenario1:

28.8 

kbpsradiobearerservice

8.2.Scenario2:

57.6 

kbpsradiobearerservice

8.2.1.Streamingserviceclass

8.3.Scenario3:

64 

8.3.1.Streamingserviceclass

8.4.Scenario4:

128 

8.4.1.Streamingserviceclass

8.5.Scenario5:

144 

8.