Michael Wong-释放AI的潜能：应对变幻莫测的AI硬件和软件

MichaelWongCodeplay技术VPC++嵌入式开发委员会SG14与机器学习委员会SG19主席，同时担任C++语言方向演化委员会主席，Codeplay研发副总裁，C++标准委员会加拿大代表团团长。Michael在C++并行计算、高性能计算、机器学习领域拥有丰富工作经验，他领导制订了应用于GPU应用开发C++异构编程语言（SYCL）标准.对Tensorflow底层性能优化有着深刻的研究和见解。其具体工作涵盖并行编程、神经网络、计算机视觉、自动驾驶等领域。Michael曾任IBM高级技术专家，领导IBMXLC++编译器、XLC编译器的开发工作。演讲主题：释放AI的潜能：应对变幻莫测的AI硬件和软件释放AI的潜能：应对变幻莫测的AI硬件和软件1133关键点acceleratorsdriving器的创新推动了人工智能的突破。硬件普及ofspecializedAIhardware专门的人工智能硬件加速器正在迅速普及。2244computedemands空前的计算需求unprecedenteddemandsforcomputationalpower.人工智能领域对计算能力的需求前所未有。框架的多元化AdiverseecosystemofAIsoftwareframeworkshas一个多元化的人工智能软件框架生态系统已经出现。TheChallengeofDiversity多元化的挑战KeyPoint:TheproliferationofAIhardware(GPUs,TPUs,FPGAs)andsoftwareframeworks(TensorFlow,PyTorch,JAX)createsacomplexecosystem.硬件多元化GPUs,TPUs,andFPGAsofferdifferentperformancecharacteristicsandoptimizationsforAIworkloads.GPUs、TPUs和FPGAs为AI工作负载提供了不同的性能特性和优化。种类繁杂的软件框架strengthsandecosystems,addingcomplexitytodevelopmentchoices.TensorFlow、PyTorch和JAX各自有独特的优势和生态系统，增加了开发选择的复杂性。IntegrationChallenges集成的挑战CombiningdiversehardwareandsoftwarecreatesintegrationchallengesforAIdevelopersandresearchers.将不同的硬件和软件结合使用，为AI开发者和研究人员带来了集成挑战。Accelerators了解人工智能加速器AIacceleratorsarespecializedhardwareforspeedingupAItasks,eachwith全球参与者KeyPoint:AIframeworksprovidethesoftwarefoundationforbuildinganddeployingAImodels.关键点：人工智能框架为构建和部署人工智能模型提供了软件基础。Framework框架KeyFeatures关键特性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.关键点：PyTorch因其灵活性和动态计算图而成为研究和快速原型设计的首选11即时执行模式便于快速调试和迭代。22算图实现。33WideAdoption在学术界和研究社区中广受认可。关键点：TensorFlow和JAX专为大规模AI设计，提供对分布式计算和多样化硬件的强大支持。industrial-scaleappJAXCapabilitiesJAXsupportsfunctionalprogrammingandautomaticdifferentiJAX支持函数式编程和自动微分。algebracomputations.关键点：PaddlePaddle是百度开发的领先AI框架，针对超大型AI进行了优化，在中国得到了广泛应用。 专注于超大型AI模型的可扩展性。 对用户友好的界面和中文文档。33Adoption在中国的研究和行业中得到广泛应AlgebraXLA:加速线性代数operatorfusionandmemoryJAXforperformancegains.compilerthatoptimizeslinearalgebracomputations,thefoundationofmanyAI多AI算法的基础。特性：支持CPU、GPU和TPU的跨平台优化；高级优化技术，如操作融合和内存布局优化；对TensorFlow和JAX的性能提升至关重要。关键点：IREE是一个创新的框架，利用MLIR在各种硬件平台上优化AI模型。1optimizationandcode22FrameworkSupportSupportsframeworks3CommunityAdoption在开源AI社区中获得支持。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.关键点：边缘AI需要像ExecuTorch和TensorFlowLite这种专为资源受限的移动和嵌入式设备设计的框架。versionofTensorFlowforefficienton-TensorFlowLite是TensorFlow的轻量级版本，用于高效的设备端机器学习。modelsformobiledevices.应移动设备。Crucialforapplicationslikemobileassistants,smartcameras,andwearables.适用于移动助手、智能摄像头和可穿戴设备等。ONNXisanopenstandardforrepresentingAImodels,enablinginteroperabilitybetweenframeworksandsimplifyinghardwaredeployment.关键点：ONNX是一个开放标准，支持AI模型的互操作性和跨平台部署。特点包括：允许在一个框架（如PyTorch）中训练模型并在另一个框架（如FeaturesofONNXdeploymentinanother(e.g.,TensorFlow).•Supportscross-platformcompatibility.•Growingecosystemoftoolsandlibraries.workloadswithhigh-dimensional关键点：PolyBlocks是一个专门的编译器，擅长优化处理高维数据的AI工作负载，这在深度学习模型中非常常见。PolyhedralOptimizationtechniquesforefficienttensor使用多面体优化技术高效处理HardwareSupportspecializedAIaccelerators.速器等硬件。Application对于前沿的深度学习模型具有TVM：端到端优化compilerstackforoptimizingand关键点：TVM是一个全面的编译器栈，用于在广泛的硬件平台上优化和部署AI模型。-Automatictuningcapabilitiesfor自动调优能力：支持在不同平台上高效部署。customizationandextension.模块化设计：允许定制和扩展。被多家公司用于生产部署。ProvidesamatureecosystemforGPUs,extensivetools.其高性能和丰富的工具而闻名。Isanopen-sourcealternativetoCUDA,offeringamoreflexibleandopenapproach.是CUDA的开源替代方案，提供更灵活和开放的方法。Isasingle-sourceC++approenablingdeveloperstowritecodethatincludingCPUs,GPUs,andotheraccelerators.采用单源C++方法，使开发者能够编写可在多种硬件（包括CPU、GPU和其他加速器）上运行的代码。accelerationforcomputervisionandgeneralpurposeGPUcomputing.关键点：OpenVX和Vulkan通过加速计算机视觉和通用GPU计算，扩展了AI领域。11APIforcomputervisionandMLacceleration,wellsuitedforedg22Initiallyforgraphics,nowwidelyu兼容性挑战KeyPoint:ThediversityofAIhardwareandsoftwarepresentscompatibilitychallenges,requiringconsiderationofframeworksupport,hardware-specificlibraries,andoptimizationtechniques.关键点：AI硬件和软件的多样性带来了兼容性挑战，需要考虑框架支持、硬件特定库和优化技术。 CompatibilitybetweenAIframeworksandhardwareiscrucialforperformance.AI框架与硬件之间的兼容性对性能至关重要。 Frameworksupportvaries,assomerequirespecificlibrariesorconfigurations.不同框架的支持程度各异，有些框架需要特定的库或配置。KeyPoint:VarioustechniquesexistforoptimizingAIperformspecificlibraries,mixed-precisiontraining,anddatapipelineoptimization.Hardware-specificLibrariesLibrarieslikecuDNNcansignificantlyacceleratecomputations.硬件特定库如cuDNN可以显著加速计算。Mixed-precisionTraining(nextslide)Allowsfastertrainingwithmaintainedaccuracy.混合精度训练（详见下一页）：在保持精度的同时加快训练速度。DataPipelineOptimizationReducesdatatransferbottlenecks.数据管道优化能够减少数据传输瓶颈。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.关键点：选择合适的AI框架和硬件需要仔细考虑项目需求、兼容性、可扩展性和预算。1233Considermodelcomplexity,datasetsize,andperformanceneeds.项目需求：考虑模型复杂度、数据集大小和性能需求。Evaluateframework-hardwarecompatibility.兼容性：评估框架与硬件的兼容性。Considerscalabilityandbudgetconstraints.还需要考虑可扩展性和预算限制。KeyPoint:NVIDIA,AMD,Intel,andGooglearekeyplayersintheAIhardwaremarket,eachwithitsstrategiesandofferings.关键点：NVIDIA、AMD、Intel和Google是AI硬件市场的主要参与者，各自拥有不同的战略和产品。DominateswithpowerfulGPUsandCUDAecosystem.DevelopingAIhardwareandsoftwaresolutions.AMDTPUsdeliverhighperformancefordeepthefutureofAIbyenhancingcross-platformcompatibilityandoptimization.正在塑造AI的未来。12233SimplifiesAImodeldeploymentonvarioushardwarebackends.Triton简化了在各种硬件后端上的AI模型部署。UsesMLIRforcross-platformcompatibility.EnablesmodularityandextensibilityinAIcompilerinfrastructure.MLIR在AI编译器基础设施中实现模块化和可扩展性。AI的未来：统一的生态系统workflows. 消除不同框架和硬件之间的壁垒。 如ONNX等标准化工作对于实现统一至关重要。33Aunifiedecosystemwillaccele统一的生态系统将促进创新的快速KeyPoint:Developersshouldadoptbestpracticeslikehybridoptimization,leveragingoptimizedlibraries,andutilizingframework-levelandruntimeoptimizations.关键点：开发者采用最佳实践，如混合优化、利用优化库以及框架级和运行时优化。Hybridoptimizationcombinesvarioustechniquesformaximumperformance.混合优化结合了多种技术以实现最大性能。UseoptimizedlibrarieslikecuDNNforspecifichardware.使用针对特定硬件优化的库，例如cuDNN。Employframework-levelandruntimeoptimizations.采用框架级和运行时优化措施。KeyPoint:FuturetrendsinAIincludeincreasedautomationofoptimizationandamoreopenandunifiedecosystem.关键点：AI的未来趋势包括优化过程的自动化增强以及更加开放和统一的生态系统。 QuantumAccelerationEmergingtechnologyforAIacceleration.量子加速：用于AI加速的新兴技术。NeuromorphicComputingBrain-inspiredcomputingforAItask