MichaelWongCodeplay技術(shù)VPC++嵌入式開發(fā)委員會SG14與機器學習委員會SG19主席，同時擔任C++語言方向演化委員會主席，Codeplay研發(fā)副總裁，C++標準委員會加拿大代表團團長。Michael在C++并行計算、高性能計算、機器學習領(lǐng)域擁有豐富工作經(jīng)驗，他領(lǐng)導(dǎo)制訂了應(yīng)用于GPU應(yīng)用開發(fā)C++異構(gòu)編程語言（SYCL）標準.對Tensorflow底層性能優(yōu)化有著深刻的研究和見解。其具體工作涵蓋并行編程、神經(jīng)網(wǎng)絡(luò)、計算機視覺、自動駕駛等領(lǐng)域。Michael曾任IBM高級技術(shù)專家，領(lǐng)導(dǎo)IBMXLC++編譯器、XLC編譯器的開發(fā)工作。演講主題：釋放AI的潛能：應(yīng)對變幻莫測的AI硬件和軟件釋放AI的潛能：應(yīng)對變幻莫測的AI硬件和軟件1133關(guān)鍵點acceleratorsdriving器的創(chuàng)新推動了人工智能的突破。硬件普及ofspecializedAIhardware專門的人工智能硬件加速器正在迅速普及。2244computedemands空前的計算需求unprecedenteddemandsforcomputationalpower.人工智能領(lǐng)域?qū)τ嬎隳芰Φ男枨笄八从小？蚣艿亩嘣疉diverseecosystemofAIsoftwareframeworkshas一個多元化的人工智能軟件框架生態(tài)系統(tǒng)已經(jīng)出現(xiàn)。TheChallengeofDiversity多元化的挑戰(zhàn)KeyPoint:TheproliferationofAIhardware(GPUs,TPUs,FPGAs)andsoftwareframeworks(TensorFlow,PyTorch,JAX)createsacomplexecosystem.硬件多元化GPUs,TPUs,andFPGAsofferdifferentperformancecharacteristicsandoptimizationsforAIworkloads.GPUs、TPUs和FPGAs為AI工作負載提供了不同的性能特性和優(yōu)化。種類繁雜的軟件框架strengthsandecosystems,addingcomplexitytodevelopmentchoices.TensorFlow、PyTorch和JAX各自有獨特的優(yōu)勢和生態(tài)系統(tǒng)，增加了開發(fā)選擇的復(fù)雜性。IntegrationChallenges集成的挑戰(zhàn)CombiningdiversehardwareandsoftwarecreatesintegrationchallengesforAIdevelopersandresearchers.將不同的硬件和軟件結(jié)合使用，為AI開發(fā)者和研究人員帶來了集成挑戰(zhàn)。Accelerators了解人工智能加速器AIacceleratorsarespecializedhardwareforspeedingupAItasks,eachwith全球參與者KeyPoint:AIframeworksprovidethesoftwarefoundationforbuildinganddeployingAImodels.關(guān)鍵點：人工智能框架為構(gòu)建和部署人工智能模型提供了軟件基礎(chǔ)。Framework框架KeyFeatures關(guān)鍵特性PyTorchTensorFlowStaticgraphs,production-readyLarge-scaledeploymentJAXFunctionalprogramming,auto-diffScientificcomputing,researchdefdeftensorflow_example():withtf.device('/GPU:0'):#Explicitdeviceplacementmodel=tf.keras.Sequential([tf.keras.layers.Dense(256,activation='relu'),tf.keras.layers.Dense(10)defpaddlepaddle_example():model=paddle.nn.Sequential(paddle.nn.Linear(784,256),paddle.nn.ReLU(),)#ModernAIFrameworkComparis#SimplifiedFrameworkComparisondefpytorch_example():model=torch.nn.Sequential(torch.nn.Linear(784,256),torch.nn.ReLU(),).to('cuda')#Hardwareaccelerationflexibilityanddynamiccomputationgraphs.關(guān)鍵點：PyTorch因其靈活性和動態(tài)計算圖而成為研究和快速原型設(shè)計的首選11即時執(zhí)行模式便于快速調(diào)試和迭代。22算圖實現(xiàn)。33WideAdoption在學術(shù)界和研究社區(qū)中廣受認可。關(guān)鍵點：TensorFlow和JAX專為大規(guī)模AI設(shè)計，提供對分布式計算和多樣化硬件的強大支持。industrial-scaleappJAXCapabilitiesJAXsupportsfunctionalprogrammingandautomaticdifferentiJAX支持函數(shù)式編程和自動微分。algebracomputations.關(guān)鍵點：PaddlePaddle是百度開發(fā)的領(lǐng)先AI框架，針對超大型AI進行了優(yōu)化，在中國得到了廣泛應(yīng)用。 專注于超大型AI模型的可擴展性。 對用戶友好的界面和中文文檔。33Adoption在中國的研究和行業(yè)中得到廣泛應(yīng)AlgebraXLA:加速線性代數(shù)operatorfusionandmemoryJAXforperformancegains.compilerthatoptimizeslinearalgebracomputations,thefoundationofmanyAI多AI算法的基礎(chǔ)。特性：支持CPU、GPU和TPU的跨平臺優(yōu)化；高級優(yōu)化技術(shù)，如操作融合和內(nèi)存布局優(yōu)化；對TensorFlow和JAX的性能提升至關(guān)重要。關(guān)鍵點：IREE是一個創(chuàng)新的框架，利用MLIR在各種硬件平臺上優(yōu)化AI模型。1optimizationandcode22FrameworkSupportSupportsframeworks3CommunityAdoption在開源AI社區(qū)中獲得支持。FlexibilityCustomizablethroughXLApassesLarge,mature(partofTensorFlow)HighlycustomizablethroughMLIRdialectsFlexibilityCustomizablethroughXLApassesLarge,mature(partofTensorFlow)HighlycustomizablethroughMLIRdialectsGrowing,focusoncross-platform/hardwaredeploymentCustomizablethroughschedulesandtemplatesLargeandactivecommunity,stronginresearchCustomizablethroughMLIRdialectsStilldeveloping,growingcommunityCustomizationCommunityandEcosystemScalabilityHigh(distributedtrainingsupport)PrimarilymodeloptimizationDesignedforscalabilityacrossdevicesDeploymenttodiversetargets,research,productionScalable(auto-tuninganddistributedcompilation)Deployment,embeddedsystems,researchHigh(leveragingMLIR)High-performanceAI,potentiallybroaderrangelaterFeatureXLATVMModular(Mojo)FeatureNotapplicable(compiler)Notapplicable(compiler)Notapplicable(compiler)Pythonic,similartoPythonProgrammingModelMulti-levelcompilation,leveragingMulti-stagecompilation,tensorexpressionbasedMLIR-based,withcustomdialectsCompilationCPUs,GPUs,TPUsCPUs,GPUs,TPUs,someNPUs,targetingmoreCPUs,GPUs,specializedacceleratorsCPUs,GPUs,potentiallytargetingmorewithMLIRHardwareAccelerationLower(strictfunctionalrequirements)High(supportsvariousframeworksandmodels)High(flexibleIRandscheduling)High(Pythonicsyntax,MLIR-basedoptimization)specializedframeworkslikedesignedforresource-constrainedmobileandembeddeddevices.關(guān)鍵點：邊緣AI需要像ExecuTorch和TensorFlowLite這種專為資源受限的移動和嵌入式設(shè)備設(shè)計的框架。versionofTensorFlowforefficienton-TensorFlowLite是TensorFlow的輕量級版本，用于高效的設(shè)備端機器學習。modelsformobiledevices.應(yīng)移動設(shè)備。Crucialforapplicationslikemobileassistants,smartcameras,andwearables.適用于移動助手、智能攝像頭和可穿戴設(shè)備等。ONNXisanopenstandardforrepresentingAImodels,enablinginteroperabilitybetweenframeworksandsimplifyinghardwaredeployment.關(guān)鍵點：ONNX是一個開放標準，支持AI模型的互操作性和跨平臺部署。特點包括：允許在一個框架（如PyTorch）中訓(xùn)練模型并在另一個框架（如FeaturesofONNXdeploymentinanother(e.g.,TensorFlow).?Supportscross-platformcompatibility.?Growingecosystemoftoolsandlibraries.workloadswithhigh-dimensional關(guān)鍵點：PolyBlocks是一個專門的編譯器，擅長優(yōu)化處理高維數(shù)據(jù)的AI工作負載，這在深度學習模型中非常常見。PolyhedralOptimizationtechniquesforefficienttensor使用多面體優(yōu)化技術(shù)高效處理HardwareSupportspecializedAIaccelerators.速器等硬件。Application對于前沿的深度學習模型具有TVM：端到端優(yōu)化compilerstackforoptimizingand關(guān)鍵點：TVM是一個全面的編譯器棧，用于在廣泛的硬件平臺上優(yōu)化和部署AI模型。-Automatictuningcapabilitiesfor自動調(diào)優(yōu)能力：支持在不同平臺上高效部署。customizationandextension.模塊化設(shè)計：允許定制和擴展。被多家公司用于生產(chǎn)部署。ProvidesamatureecosystemforGPUs,extensivetools.其高性能和豐富的工具而聞名。Isanopen-sourcealternativetoCUDA,offeringamoreflexibleandopenapproach.是CUDA的開源替代方案，提供更靈活和開放的方法。Isasingle-sourceC++approenablingdeveloperstowritecodethatincludingCPUs,GPUs,andotheraccelerators.采用單源C++方法，使開發(fā)者能夠編寫可在多種硬件（包括CPU、GPU和其他加速器）上運行的代碼。accelerationforcomputervisionandgeneralpurposeGPUcomputing.關(guān)鍵點：OpenVX和Vulkan通過加速計算機視覺和通用GPU計算，擴展了AI領(lǐng)域。11APIforcomputervisionandMLacceleration,wellsuitedforedg22Initiallyforgraphics,nowwidelyu兼容性挑戰(zhàn)KeyPoint:ThediversityofAIhardwareandsoftwarepresentscompatibilitychallenges,requiringconsiderationofframeworksupport,hardware-specificlibraries,andoptimizationtechniques.關(guān)鍵點：AI硬件和軟件的多樣性帶來了兼容性挑戰(zhàn)，需要考慮框架支持、硬件特定庫和優(yōu)化技術(shù)。 CompatibilitybetweenAIframeworksandhardwareiscrucialforperformance.AI框架與硬件之間的兼容性對性能至關(guān)重要。 Frameworksupportvaries,assomerequirespecificlibrariesorconfigurations.不同框架的支持程度各異，有些框架需要特定的庫或配置。KeyPoint:VarioustechniquesexistforoptimizingAIperformspecificlibraries,mixed-precisiontraining,anddatapipelineoptimization.Hardware-specificLibrariesLibrarieslikecuDNNcansignificantlyacceleratecomputations.硬件特定庫如cuDNN可以顯著加速計算。Mixed-precisionTraining(nextslide)Allowsfastertrainingwithmaintainedaccuracy.混合精度訓(xùn)練（詳見下一頁）：在保持精度的同時加快訓(xùn)練速度。DataPipelineOptimizationReducesdatatransferbottlenecks.數(shù)據(jù)管道優(yōu)化能夠減少數(shù)據(jù)傳輸瓶頸。classclassOptimizedTraining:def__init__(self):self.model=Model()self.scaler=torch.cuda.amp.GradScaler()deftraining_step(self,data):withtorch.cuda.amp.autocast():#Automaticmixedprecisionoutput=self.model(data)loss=criterion(output)self.scaler.scale(loss).backward()self.scaler.step(optimizer)self.scaler.update()KeyPoint:ChoosingtherightAIframeworkandhardwarerequirescarefulconsiderationofprojectrequirements,compatibility,scalability,andbudget.關(guān)鍵點：選擇合適的AI框架和硬件需要仔細考慮項目需求、兼容性、可擴展性和預(yù)算。1233Considermodelcomplexity,datasetsize,andperformanceneeds.項目需求：考慮模型復(fù)雜度、數(shù)據(jù)集大小和性能需求。Evaluateframework-hardwarecompatibility.兼容性：評估框架與硬件的兼容性。Considerscalabilityandbudgetconstraints.還需要考慮可擴展性和預(yù)算限制。KeyPoint:NVIDIA,AMD,Intel,andGooglearekeyplayersintheAIhardwaremarket,eachwithitsstrategiesandofferings.關(guān)鍵點：NVIDIA、AMD、Intel和Google是AI硬件市場的主要參與者，各自擁有不同的戰(zhàn)略和產(chǎn)品。DominateswithpowerfulGPUsandCUDAecosystem.DevelopingAIhardwareandsoftwaresolutions.AMDTPUsdeliverhighperformancefordeepthefutureofAIbyenhancingcross-platformcompatibilityandoptimization.正在塑造AI的未來。12233SimplifiesAImodeldeploymentonvarioushardwarebackends.Triton簡化了在各種硬件后端上的AI模型部署。UsesMLIRforcross-platformcompatibility.EnablesmodularityandextensibilityinAIcompilerinfrastructure.MLIR在AI編譯器基礎(chǔ)設(shè)施中實現(xiàn)模塊化和可擴展性。AI的未來：統(tǒng)一的生態(tài)系統(tǒng)workflows. 消除不同框架和硬件之間的壁壘。 如ONNX等標準化工作對于實現(xiàn)統(tǒng)一至關(guān)重要。33Aunifiedecosystemwillaccele統(tǒng)一的生態(tài)系統(tǒng)將促進創(chuàng)新的快速KeyPoint:Developersshouldadoptbestpracticeslikehybridoptimization,leveragingoptimizedlibraries,andutilizingframework-levelandruntimeoptimizations.關(guān)鍵點：開發(fā)者采用最佳實踐，如混合優(yōu)化、利用優(yōu)化庫以及框架級和運行時優(yōu)化。Hybridoptimizationcombinesvarioustechniquesformaximumperformance.混合優(yōu)化結(jié)合了多種技術(shù)以實現(xiàn)最大性能。UseoptimizedlibrarieslikecuDNNforspecifichardware.使用針對特定硬件優(yōu)化的庫，例如cuDNN。Employframework-levelandruntimeoptimizations.采用框架級和運行時優(yōu)化措施。KeyPoint:FuturetrendsinAIincludeincreasedautomationofoptimizationandamoreopenandunifiedecosystem.關(guān)鍵點：AI的未來趨勢包括優(yōu)化過程的自動化增強以及更加開放和統(tǒng)一的生態(tài)系統(tǒng)。 QuantumAccelerationEmergingtechnologyforAIacceleration.量子加速：用于AI加速的新興技術(shù)。NeuromorphicComputingBrain-inspiredcomputingforAItask