2024 全球6G技术大会 -10.0E Edge-Native Intelligence White Paper

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Abstract

Edgecomputing,asakeytechnologyofthenextgenerationofradioaccess

networks(RAN),hasdriventhedecentralizationofnetworksandcomputingfacilities.Edgeserversclosertouserterminalscansignificantlyreduceservicelatencyandcope

withemergingnewscenarios.Simultaneously,therapiddevelopmentofartificial

intelligence(AI)playsasignificantroleinenhancingtheperformanceofedge

computing,aidingedgedevicesincopingwiththerapidlyincreasingdataontheedge.

Therefore,combiningthelocalcomputingcapabilityofedgedatawiththestrong

computingcapabilitiesofAI,knownasedgeintelligence,canenhancethedata

processingcapabilitiesontheedge,improvetheoverallperformanceofwireless

communicationsystems,andenhanceuserserviceexperiences.Edgeintelligenceisahotandrapidlydevelopingfieldinrecentyears,andthiswhitepaperaimstoanalyze

thecurrentresearchprogressinedgeintelligence.Itmainlyincludes:

(1)6GEdgeIntelligenceNetworksandInfrastructure:Firstly,theedge-native

intelligentarchitecturefor6Gnetworksisanalyzed.Then,theedgeintelligence

computinginfrastructureisintroduced,includingedgeintelligenthardwareandcloudplatforms.Finally,theedgeintelligencenetworkinfrastructureisdescribed,including

theedgeintelligenceaccessnetworkandcorenetwork.

(2)KeyTechnologiesofEdge-NativeIntelligence:Itisintroducedfromthe

aspectsofmodellightweight,edge-cloudcollaborativeintelligence,edgeintelligentdeployment,anddeepedgenodes.Edgeintelligenceinwirelessfederatedlearningis

alsoexplainedindetail,includingmodelsparsificationandmodelquantizationin

federatedlearning.

(3)ApplicationsofEdge-NativeIntelligence:Typicalapplicationsofedge-native

intelligenceareanalyzed,suchassmarttransportation,smartmanufacturing,and

smartenergysaving.

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Contents

1.Introduction 3

1.1Background 3

1.2OverviewofEdgeComputingandEdge-NativeIntelligence 4

1.3ImportanceofEdge-NativeIntelligence 5

2.6GEdgeIntelligenceNetworksandInfrastructure 7

2.1Edge-NativeIntelligenceArchitecturefor6G 7

2.2EdgeIntelligenceComputingInfrastructure 12

2.3EdgeIntelligenceNetworkInfrastructure 37

3.KeyTechnologiesofEdge-NativeIntelligence 59

3.1ModelLightweighting 59

3.2Edge-CloudCollaborativeIntelligence 68

3.3WirelessFederatedLearninginEdgeIntelligence 75

3.4EdgeIntelligenceDeployment 85

3.5DeepEdgeNodes 91

4.Edge-NativeIntelligenceApplications 101

4.1SmartTransportation 101

4.2SmartManufacturing 110

4.3IntelligentEnergySaving 118

5.DevelopmentandChallengesofEdge-NativeIntelligence 120

6.Acknowledgment 123

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1.Introduction

1.1Background

From1Gto5G,communicationtechnologyhasundergonemultipleupgradesandtransformations,significantlyimprovingdatatransferrates,reducinglatency,andexpandingnetworkcoverage.However,withtherapiddevelopmentoftechnologiessuchastheInternetofThings(IoT)andAI,theInternetofEverythingandincreasinglycomplexapplicationscenariosposechallengesthatexistingnetworkarchitecturescannotmeet.Therefore,asthenextgenerationofcommunicationtechnology,6Gmustpossesshigherperformanceandmorepowerfulintelligentcapabilities,drivingthetransitionofedge-sidenetworksfrom"InternetofEverything"to"IntelligenceofEverything."Tobetteradapttofuturediverseandcomplexuserrequestsandapplicationscenarios,theconceptofedge-nativeintelligencecameintobeingbyintegratingintelligenttechnologyintothedesignand

implementationofcommunicationsystems.[1]

Inrecentyears,thetheoryandtechnologyofAIhaveprogressedandfoundwidespreadapplicationinindustrialscenarios.However,mostAIservicesaretypicallydeployedoncloudservers.Withtheadventofthe"InternetofEverything"era,thenumberofterminaldevicesandtheamountofdatageneratedareincreasingrapidly.Thecentralizeddataprocessingmethod,whichuploadsalldatatothecloud,cannotmeetthelow-latencyrequirementsofusers.Consequently,edgecomputingemergedwiththedevelopmentoftheInternetofThings(IoT)andAI.However,currentresearchonedgecomputingimplementationfailstomeetthedemandsofcomplexservicescenarios.Therefore,edge-nativeintelligencehasthepotentialto

becomethenextresearchhotspotinedgecomputing.[2]

Edge-nativeintelligenceenablesself-dynamicsensingandself-optimizationcapabilitiesbetweenvariousunitsinthenativenetwork.Itbreaksawayfromthe

traditionalplug-inAIarchitecturebydeeplyintegratingAIintovariouslayersofthe

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networktoenhancetheoverallsystemnetworkefficiency.Itachievesanautonomoussensingoftheoveralllifecycleandself-managementwithinthenetwork

architecture.[3]

1.2OverviewofEdgeComputingandEdge-Native

Intelligence

Edgecomputing:Theconceptofedgecomputingisintroducedtoalleviatetheprocessingpressureonclouddatacenters.Itisatechnologythatmigratescomputingprocessesfromcentralserverstotheedgeofdevices.Thecoreideaistointegratenetwork,computing,storage,andapplicationservicesintoaplatformclosetothedatasource,enablingservicestobeprovidednearby.Thistechnologyhelpsreducetheprocessingloadofcloudcomputingandaddressestheissueofdatatransferlatency,meetinguserneedsinreal-timeservice,intelligentapplications,security,andprivacy

protection.

Edge-nativeintelligence:Edgeintelligenceisthenextstageofdevelopmentaftertheevolutionofedgecomputing.WiththerapiddevelopmentanditerationofedgecomputingandAItechnologies,theconceptofedgeintelligencecameintobeing.ItexecutesAIalgorithmsattheedge,whichisamorecomplexdataanalysistask.DeployingAIapplicationsonedgenodes,especiallyonmobiledevicesandIoTdevices,requiresthesupportofedgecomputing.Firstly,edgenodesneedtoprovidecorrespondinghardwareandprogramminglibrariestomeetthebasicoperationsofAI.Secondly,anedgecomputingplatformisneededforresourcemanagementandtaskschedulingonedgenodes.Finally,itisnecessarytosolvethetaskoffloadinganddata

securityproblemsincloud-basedcollaborativeAI.[4]

AsAItechnologycontinuestoevolve,thelevelofintelligenceinedgedeviceshasbeenelevated.Initially,edgeintelligenceprimarilyfocusedonrunningAIalgorithmsandmodelsonedgedevicestoachieverapiddataprocessingandresponse.Thisapproachhadarelativelylowlevelofintelligencebecausethefunctionalityand

performanceofedgedeviceswerelimited,preventingtheexecutionofcomplexAI

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algorithmsandmodels.[5]Withongoingtechnologicaladvancements,theperformanceandintelligenceofedgedeviceshavesignificantlyimproved.Inthisprocess,theconceptofedge-nativeintelligencehasgraduallyemerged.Edge-nativeintelligenceemphasizestheintegrationofAItechnologyintoedgedevices,enablingthemtohaveautonomousdataprocessingandanalysiscapabilities.Thisapproachenablesedgedevicestobetteradapttocomplexapplicationscenarios,andimprovethespeedand

efficiencyofdataprocessingandresponse.[6]

1.3ImportanceofEdge-NativeIntelligence

Theimportanceofedge-nativeintelligenceincludesthefollowingaspects:

(1)FullUnleashingofDataPotentialattheNetworkEdgeThroughAI:Withthesurgeinthenumberofmobiledevices,amassiveamountofdata(e.g.,audio,images,andvideos)willbegeneratedatthedeviceend.TheintroductionofAIalgorithmswillbeessentialatthispoint,astheycanquicklyanalyzetheselargevolumesofdataandextractfeaturesfromthem,leadingtohigh-qualitydecision-makingandimprovedefficiencyandreliabilityofdataprocessing.Thishelpstoreducemanualintervention

anderrorrates,improvingserviceefficiencyandreliability.[7]

(2)ExpansionofIntelligentAlgorithmDeploymentScopewithRicherDataandApplicationScenarios:Inthetraditionalcloudcomputingmodel,datasourcesaregenerallyuploadedandstoredinthecloudduetoitsextremelyhighcomputingperformance.[8]However,withtherapiddevelopmentoftheInternetofEverythingera,thetraditionalcloudcomputingmodelisgraduallyshiftingtowardstheedgecomputingmodel.Inthefuture,theedgesidewillgenerateamassiveamountofIoTdata.IfallofthisdataneedstobeuploadedtothecloudforAIalgorithmprocessing,itwilloccupyalargeamountofbandwidthresourcesandputagreatdealofcomputingpressureonthecloudcomputingdatacenter.Toaddressthesechallenges,offloadingcloudcomputingpowertotheedgeenableslow-latencydataprocessing,

thusachievingahigh-performanceedgeintelligenceprocessingmodel.[9]

(3)BetterSystemAvailabilityandScalabilitywithEdge-NativeIntelligence:AI

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technologyhasachievedtremendoussuccessinmanydigitalproductsandservicesindailylife,suchasvideosurveillanceandsmarthomes.AIisalsoacriticaldrivingforceattheforefrontofinnovation,includingareaslikeautonomousdrivingandsmartfinance.Therefore,AIshouldbeclosertopeople,data,andterminaldevices.Intheprocessofachievingthesegoals,asdataprocessingoccurslocally,edgedevicescancontinuetooperateevenifthecentralserverencountersissues.Additionally,withtheadditionofnewapplicationsorupgradestoexistingones,edgedevicescaneasily

expandormodify,providinggreaterflexibility.

(4)EnhancedAvailabilityandAccessibilityofAIApplications:Withtheenhancedprocessingcapabilitiesofedgedevices,moreAIapplicationscanrunonthe

devicesthemselves,ratherthanrelyingsolelyoncloudservers.Thisincreasesthe

usabilityandaccessibilityofAI.[10]

References

[1]S.Talwar,N.Himayat,H.Nikopour,F.Xue,G.WuandV.Ilderem,“6G:ConnectivityintheEraofDistributedIntelligence,”IEEECommunicationsMagazine,vol.59,no.11,pp.45-50,Nov.2021.

[2]M.ElsayedandM.Erol-Kantarci,“AI-EnabledFutureWirelessNetworks:Challenges,Opportunities,andOpenIssues,”IEEEVehicularTechnologyMagazine,vol.14,no.3,pp.70-77,Sep.2019.

[3]S.Deng,H.Zhao,W.Fang,J.Yin,S.DustdarandA.Y.Zomaya,“EdgeIntelligence:TheConfluenceofEdgeComputingandArtificialIntelligence,”IEEEInternetofThingsJournal,vol.7,no.8,pp.7457-7469,Aug.2020.

[4]M.Pan,W.SuandY.Wang,“ReviewofResearchontheCurriculumforArtificialIntelligenceandIndustrialAutomationbasedonEdgeComputing,”2021InternationalConferenceonNetworkingandNetworkApplications(NaNA),LijiangCity,China,2021,pp.222-226.

[5]Y.Xiao,G.Shi,Y.Li,W.SaadandH.V.Poor,“TowardSelf-LearningEdgeIntelligencein6G,”IEEECommunicationsMagazine,vol.58,no.12,pp.34-40,Dec.2020..

[6]H.HuandC.Jiang,“EdgeIntelligence:ChallengesandOpportunities,”2020InternationalConferenceonComputer,InformationandTelecommunicationSystems(CITS),Hangzhou,China,2020,pp.1-5.

[7]M.Mukherjee,R.Matam,C.X.Mavromoustakis,H.Jiang,G.MastorakisandM.Guo,“IntelligentEdgeComputing:SecurityandPrivacyChallenges,”IEEECommunicationsMagazine,vol.58,no.9,pp.26-31,Sep.2020.

[8]Y.Sun,B.Xie,S.ZhouandZ.Niu,“MEET:Mobility-EnhancedEdgeinTelligenceforSmartandGreen6GNetworks,”IEEECommunicationsMagazine,vol.61,no.1,pp.64-70,Jan.2023.

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[9]Q.Cui,Z.Gong,W.Ni,Y.Hou,X.Chen,X.Tao,P.Zhang,“StochasticOnlineLearningforMobileEdgeComputing:LearningfromChanges,”IEEECommunicationsMagazine,vol.57,no.3,pp.63-69,Mar.2019.

[10]M.Yao,M.Sohul,V.MarojevicandJ.H.Reed,“ArtificialIntelligenceDefined5GRadioAccessNetworks,”IEEECommunicationsMagazine,vol.57,no.3,pp.14-20,Mar.2019.

2.6GEdgeIntelligenceNetworksand

Infrastructure

2.1Edge-NativeIntelligenceArchitecturefor6G

Asakeyenablingtechnologyforthenextgenerationofradiowirelessnetworks,Multi-accessEdgeComputing(MEC)cansupportaplethoraofemergingservices.WiththecontinuousdevelopmentofAI,itsapplicationinMECisbecomingincreasinglywidespread.However,in5Gnetworks,AIisonlyusedasanadd-onapplicationtoassistMEC.In6Gnetworks,MECwillincorporateAIfromtheinitialdesignphase,treatingitasanintegralpartoftheMECsystem.ThisapproachaimstoenhancetheflexibilityandopennessofMEC,betteraddressingtheconstantlyemergingapplicationscenariosanduserdemands.Asaresult,theedge-nativeintelligencearchitecturehasbeenproposed,whichisbasedonthedecouplingand

reconstructionofAIfunctionstoprovideuserswithcustomizedAIservices.

2.1.1OverviewoftheArchitecture

Theedge-nativeintelligencearchitectureconsistsof"fourlayersandthreeplanes",asshowninFigure2.1.The"fourlayers"includetheinfrastructurelayer,virtualizationlayer,functionlayer,andapplicationlayer;the"threeplanes"include

thecontrolplane,AIplane,andmanagementandorchestration(MANO)plane.

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Figure2.1Edge-NativeIntelligenceArchitecture

I.Fourlayers:

.Infrastructurelayer:Locatedatthebottomoftheedge-nativeintelligencearchitecture,itencompassesallcommunication,storage,andcomputingresourcesinthesystem.CommunicationresourcesincludeWi-FiandtheInternet;storageresourcesincludememory,HardDiskDrive(HDD)andSolidStateDrive(SSD);computingresourcesincludeCentralProcessingUnit(CPU)andGraphics

ProcessingUnit(GPU).

.Virtualizationlayer:Positionedabovetheinfrastructurelayer,itabstractstheunderlyingresourcesintoaresourcepoolforusebyupper-layernetworkfunctions.Whenservicedemandsarise,thevirtualizationlayercancreateDockercontainersandrunthemintheresourcepooltosupplynetworkfunctions,

ensuringtheirnormaloperationandtherebyguaranteeingcustomizedAIservices.

.Functionlayer:Locatedabovethevirtualizationlayer,itconsistsofdecouplednetworkfunctions,namelycontrolfunctionsandAIfunctions,andaservicebus.Differentnetworkfunctionscanbeactivated,released,andreconfiguredinreal

timebasedonservicerequirements,interconnectedthroughtheservicebus.

.Applicationlayer:Locatedatthetopoftheedge-nativeintelligencearchitecture,itincludesdiversenetworkapplications.Theapplicationlayerinteractsdirectly

withusersand,uponuserrequests,automaticallyinvokesthenetworkfunctions

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ofthefunctionlayerandtheDockercontainersofthevirtualizationlayerto

provideservicestousers.

II.Threeplanes:

.Controlplane:Itisresponsibleforthetransmissionandprocessingofcontrol

signalingfromtheinfrastructurelayertotheapplicationlayer.

.MANOplane:IttransformsservicerequestsfromthecontrolplaneintoMANOcommandsandcoordinatesandmanagesthesystem'sfunctionsandresources.TheMANOplaneincludestheVirtualizedInfrastructureManager(VIM),FunctionalMANO,andApplicationMANO,dedicatedtothemanagementand

orchestrationofresources,functions,andapplications,respectively.

.AIplane:AlsoknownasthenativeAIplane,itservesasthecoreaspectoftheedge-nativeintelligencearchitecture,responsibleforlearninguserandnetworkbehavioranddemands,achievingself-operationofthenetwork.ItsvirtualizationlayerprovidesaruntimeenvironmentlibraryforAIapplications,suchasPyTorchandTensorFlow,whichcanbeselectedbasedonapplicationrequestsandresourcestate.TheAIplaneincludesdecoupledAIfunctionsandaservicebusinitsvirtualizationlayer,whileitsapplicationlayercomprisesatemplateselectorandanintelligentalgorithmmodellibraryforflexiblereconstructionof

edge-nativeintelligence.

2.1.2DesignandImplementationoftheNativeAIPlane

Intheedge-nativeintelligencearchitecture,themicroservice-basedAIplaneisdecoupledintoindependentAIfunctions.TheseAIfunctionscanbeactivatedandinvokedondemand.Whenanapplicationrequestarrives,thedecoupledAIfunctionscanbecombinedondemandtoprovideAIservicestousers,thusachieving

edge-nativeintelligence.

I.Decouplingofedge-nativeintelligenceplane:

AsshowninFigure2.1,intheedge-nativeintelligenceplane,AIservicesare

decoupledintoDataCollectionFunction(DCF),DataPreprocessingFunction(DPF),

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ModelTrainingFunction(MTF),ModelValidationFunction(MVF),andData

StorageFunction(DSF).Eachfunctionisdescribedasfollows:

.DCF:CollectsrawdatarequiredforAImodeltrainingandgeneratesthe

correspondingtrainingdataset.

.DPF:Preprocessestherawdatacontaininginvalidcomponents.Removesinvalidoroffsetcontentfromtherawdatathroughdatasampling,featureextraction,anddimensionalityreduction.ConvertsthedataintotheformatrequiredforAImodel

training.

.MTF:SelectstheappropriateAIalgorithmaccordingtoservicerequirementsandtrainsthecoremodeloftheAIalgorithm.

.MVF:EvaluatestheperformanceoftheAImodelduringmodeltrainingor

real-timeinference.

.DSF:StoresandmanagesalldataandAIF-relatedparametersoftheAIplane.

CommunicationandinteractionbetweendifferentAIfunctionsoccurthroughaunifiedservicebus.Additionally,AIfunctionscancommunicatewithcontrolfunctionsviatheservicebusandbeactivatedbyFunctionalMANObasedonservice

types.

II.Reconstructionofedge-nativeintelligenceplane:

Edge-nativeintelligencereconstructionborrowstheideaoftemplateand

instantiation.ItperformsAIfunctionactivation,runtimeconfiguration,andresource

allocationbasedonservicetypetoachievecustomizedAIservices.

.Template:Providesacommonsolutionforaclassofedgeintelligentservicesbyextractingandabstractingtheircommonalities.Theedge-nativeintelligencetemplateinvolveskeyelementssuchastemplateinformation(Tinf)andtemplateidentifier(Tid).TemplateinformationencompassesthecomponentsoftheAIapplication,namelythetypesofAIF,requiredresources,andruntimeenvironments,storedintheintelligentalgorithmmodellibrary.ThetemplateidentifierdistinguishesdifferenttemplatescorrespondingtoAIapplicationsandisstoredinthetemplateselector.Beforeusingthetemplate,predefinedoperations

arenecessary,definingparametersrelatedtofunctionalityactivation,resource

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allocation,andruntimeenvironmentconfigurationaccordingtospecificAI

applicationrequirements.

.Instantiation:CreatesanAIapplicationinstancebasedontheparametersdefinedinthetemplatetorespondtoAIservicerequests.AsshowninFigure2.2,

theedge-nativeintelligenceinstantiationprocessincludesthefollowingsteps:

1)MANOcontinuouslymonitorstheapplicationlayerandsendsatemplate

selectionrequesttothetemplateselectorwhenanapplicationrequestisreceived.

2)ThetemplateselectorselectsthecorrespondingtemplateaccordingtotheapplicationtypeandsendsitsTidtotheintelligentalgorithmmodellibraryto

requestTinf.

3)TheintelligentalgorithmmodellibraryextractsthecorrespondingTinf

ofthetemplateandprovidesfeedbacktothetemplateselector.

4)ThetemplateselectorsendsthereceivedTinftotheMANOplane.

5)TheMANOplaneperformstheinstantiationoperationaccordingtothe

receivedTinf:

(a)Configurestheruntimeenvironmentlibraryrequiredbytheapplication.

(b)Allocatestherequiredresources.

(c)ActivatestherelevantAIF.

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Figure2.2Edge-NativeIntelligenceInstantiationProcess

2.2EdgeIntelligenceComputingInfrastructure

2.2.1EdgeIntelligentHardware

Withtherapiddevelopmentoftechnology,edgeintelligenthardwarehasgraduallybecomeafocalpointwheretheIoT,AI,andcloudcomputingintersect.Thistypeofintelligenthardwarenotonlypossessesreal-timeandefficientdataprocessingcapabilitiesbutalsocanmakeintelligentdecisionsatthenetworkedge,significantlyalleviatingdataprocessingpressureonthecloudandimprovingoverallsystem

responsivenessandefficiency.

Intermsofcustomerdemands,edgeintelligenthardwarecaterstovarious

industries,placinghighrequirementsonadaptabilitytotheenvironment,real-time

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processing,security,andstability.Forexample,insmartmanufacturing,edgeintelligenthardwarecancollect,process,andanalyzevariousdataonfactoryproductionlinesinreal-time,enablingautomationandintelligenceintheproductionprocess.Inthemedicalfield,edgeintelligenthardwarecananalyzepatients'

physiologicaldata,enablingremotehealthcareandintelligentdiagnosis.

Fromatechnicalperspective,edgeintelligenthardwareincorporatesadvancedalgorithmsanddataprocessingtechnologies,enablinghigh-efficiencydataprocessingandanalysis.Additionally,itadoptsamultitudeofsensors,communicationtechnologies,andsoftwaredefinitions,achievinginterconnectednessandinteroperabilitywithvariousdevicesandsystems.Moreover,edgeintelligenthardwarestandsoutwithitslowpowerconsumptionandhighreliability,readily

meetingtheusagerequirementsindiverseharshenvironments.

Intermsofproductforms,edgeintelligenthardwarecanmanifestinvariousdevicessuchasintelligentcameras,intelligentsensors,intelligentrobots,andedgeservers.Thesedevicescanconnectwithvariousequipmentandsystems,facilitatingdatasharingandcollaborativeprocessing.Moreover,theycanundergoremotemanagementandcontrolthroughthecloud,enablingremotemonitoringand

maintenanceofdevices.

I.Edgeintelligenthardwarerequirements

Asshowninthetablebelow,consideringthedistancefromthehardwaredeploymentlocationtothedatacenter,edgeintelligenthardwarecanbecategorizedintoNearEdgeandFarEdge.NearEdgeprimarilyinvolvesthedescentofcloudcomputing,resemblingclouddatacentersinfunctionality,withpowerfulandcomprehensivecomputingcapabilities.Thehardwareproductformsincludeintegratedcabinetsandheavy-edgeservers.FarEdgefocusesmoreonspecificapplicationsattheedgesite,withstrongrelevancetospecificapplicationssuchasdataaggregation/transformations,protocolparsing,industrialcontrol,andAIinference.Thehardwareproductformsarediverse,includingindustrialcomputers,

PLCs,gateways,andMEC.

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Function

ProductExamples

Near

Edge

Deepedgecomputing

Regionaldatacenters,CDN(contentdeliverynetworks),telecomdatacenters,hostingservice

providers

Deepedgecomputing

Localdatacenters,heavy-edgeservers,microdata

centers(integratedcabinets)

Far

Edge

Aggregationanalysisandcontrol,data

management

AIBox,MEC,HCI(hyper-converged

infrastructure)

Aggregation,conversion,filtering,data

reduction,forwarding

Gateways,smallcells,routers,accesspoints

Analogtodigitalconversion(sensors),sendingcontroldata(actuators),direct

analysis/control

Industrialcomputers,PLC(programmablelogic

controller),DCS(distributedcontroller),etc.

Edgecomputinghardwareproductshavetheiruniquecharacteristics,distinctfromthehardwareproductsofcloudcomputingandedgecomputing.Thereasons

behindthisdistinctionaretheprimarydemandsfacedbyedgecomputing:

(Ⅰ)Diverseandcomplexapplicationscenarios:

(1)Thediversityinedgedeploymentrequiresdifferentinfrastructurecombinations.Edgedeploymentspansvariousindustryapplications,userscenarios,andverticaldomains.Itincludesawiderangeofinfrastructuresolutions,makingtheedgesolutionecosystemhighlycomplexintermsofproductforms,configurations,

andmanagementtools.

(2)Edgecomputingisexperiencingrapidgrowthinindustriessuchastelecommunications,utilities,manufacturing,andfinance.Telecomoperatorsareactivelybuildingedgecomputingplatforms,leadingmarketdevelopment.Otherindustries,particularlyutilities,manufacturing,andfinance,arealsoacceleratingthe

adoptionofedgecomputingbydeployingdedicatededgeinfrastructuretoenhance

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efficiencyinusecasessuchastheIndustrialInternet,gridmanagement,andsmart

commercialbuildings.

(3)ThevigorousdevelopmentoftechnologieslikeAI,machinelearning,bigdatamodels,andheterogeneouscomputingfurtherpropelsthegrowthoftheedgecomputingmarket.Theproliferationofcompute-intensiveanalyticalworkloadsisubiquitousinmanyindustriesandusecases,unlockingthepotentialofuntappeddata,mostofwhichresidesorisgeneratedattheedge.TheexpectedconvergenceofAI-nativecomputingcapabilitieswiththeperformancerequirementsofnewanalyticalplatformswilldrivethegrowthofmanynewedgeinfrastructuredeployments.ThediversityofAIapplicationsalsodiversifiesthedemandforedgecomputinghardware,

software,services,andsolutions.

(Ⅱ)Longlifecycleproductdemands:

(1)Inedgecomputingapplicationsacrossvariousindustriesliketransportation,healthcare,energy,andindustry,suchasrailtrafficcontrolsystems,mediumtolargemedicalequipment,substation/distributionstationcontrolunits,andindustrialcontrolDCS/MES,theproductsoftengothroughalonglifecycleinvolvingstagesofproductdesign,researchanddevelopment,testingandverification,implementationandoperation,andlatermaintenance.Therefore,5-7yearsorevenlongerlifecycleforedge