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克服现实世界中数据与AI之间的挑战.pdf

上传人: a****d 编号:184998 2024-10-07 13页 1.35MB

1、1|2024 SNIA.All Rights Reserved.Overcome Real World Challenges between Data and AISteven Yuan CEO of StorageX.ai2|2024 SNIA.All Rights Reserved.AI2.0:AGI is driving next big cycle 3|2024 SNIA.All Rights Reserved.Data Volume20102020Scaling Issues:Compute+Network+Storage Bottleneck DataCPU&GPUThis Doe

2、snt Scale!Existing compute per data volumeIdeal Compute4|2024 SNIA.All Rights Reserved.Moving Compute-Return ResultsLower TrafficLower LatencyMoving Data Heavy TrafficLong LatencyThe Dumbbell effect causing high compute costData Centric Computing is a key solutionNetworkCPU/GPUNetworkStorage 5|2024

3、SNIA.All Rights Reserved.Learning Process of ChatGPT(1)Supervised learning(2)Reinforcement learning(3)Proximal Policy Optimization(PPO)DataDataDataData Movements+Compute6|2024 SNIA.All Rights Reserved.AutomationDigitizationArtificial Intelligence Automation tools to reduce labor cost use of data to

4、achieve the intended production goals,or operational efficiency.Use intelligence for self monitoring,automated problem identification,unmanned management etc.Key Factor Cost reduction Production Cycle Time Factory Capacity Key FactorUse digital tools for businessdecision making.Example:ERP,CIM,MES e

5、tc IoT devices Edge CloudKey FactorUse AI to achieve lighthouse factory:Higher Capacity Higher yield Faster problem solving Shorter cycle time More flexibilitySmart Factory:Intelligence needed for efficiency 7|2024 SNIA.All Rights Reserved.Network between Storage&ComputeCPUDRAMPCIe SwitchNetworkGPUG

6、PUGPUGPUCompute Node Big Data Set:Large amount of data generated from AOI machinery,Million files per day,could accumulate to PB level data per week.Large files:can be more than GB per file Faster cycle time:less than seconds for decision making Real World Challenges for Data and AI PCIe SwitchSSDSS

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本文探讨了数据和人工智能(AI)在现实世界挑战中的作用。文章指出,数据中心的计算、网络和存储存在瓶颈,而数据中心的网络、CPU/GPU、存储和数据移动加计算的自动化和数字化是解决这些挑战的关键。文章强调了降低成本、缩短生产周期时间和提高工厂产能的重要性。为了实现这一目标,智能工厂需要利用AI提高效率,如提高产能、产量、问题解决速度和周期时间,以及增加灵活性。此外,数据驱动的决策可以帮助快速识别故障、解决问题,提高产量,并缩短生产爬坡时间。最后,文章提出了面向不同行业的智能工厂基础设施,以及大数据集和垂直大型模型在特定领域的重要性。
如何应对智能制造中的大数据挑战? 数据为中心的计算如何提升工厂效率? AI在智能工厂中扮演什么角色?
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