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市场调查报告书

深度学习芯片组的全球市场:用于AI学习/推理的CPU / GPU / FPGA / ASIC / SoC加速器

Deep Learning Chipsets - CPUs, GPUs, FPGAs, ASICs, and SoC Accelerators for AI Training and Inference Applications: Global Market Analysis and Forecasts

出版商 Omdia | Tractica 商品编码 948453
出版日期 内容资讯 英文 69 Pages; 88 Tables, Charts & Figures
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价格
深度学习芯片组的全球市场:用于AI学习/推理的CPU / GPU / FPGA / ASIC / SoC加速器 Deep Learning Chipsets - CPUs, GPUs, FPGAs, ASICs, and SoC Accelerators for AI Training and Inference Applications: Global Market Analysis and Forecasts
出版日期: 2020年07月02日内容资讯: 英文 69 Pages; 88 Tables, Charts & Figures
简介

人工智能(AI)加速的需求已得到广泛认可,这使得AI加速芯片组成为企业(数据中心)和边缘设备制造商的标准功能要求。结果,在过去两年中,AI芯片组的出货量和收入已显示出显著增长。全球深度学习芯片组收入预计将从2019年的114亿美元增长到2025年的712亿美元。

该报告调查了深度学习芯片组的市场,市场定义和概述,分析了影响市场增长的各种因素,技术趋势,加速需求,芯片组要求。 ,收入规模的转变和预测,按芯片组类型,学习/推理类别,计算能力,最终用户,竞争环境,主要公司的概况等各种类别细分。

执行摘要

市场分析

  • 在市场上使用AI
    • 企业中的AI加速
    • 边缘的AI加速
  • 市场细分
    • 按体系结构(芯片组类型)
    • 学习与推理
    • 通过计算能力
    • 按功耗
    • 按最终用户:Enterprise Edge
  • 市场增长因素
  • 市场障碍/问题
  • 应用程序/用法示例
  • 地区差异
  • 启动业务计划

技术分析

  • 神经网络演进与硬件加速需求
  • AI推理工作量
  • 神经网络解释和芯片组要求
  • 用于深度学习的芯片组架构
  • 新的AI加速架构
  • 芯片组技术参数
  • 基准化
  • 用于AI芯片组的数据格式
  • 深度学习开发框架

主要公司

  • Amazon
  • AMD
  • ARM
  • Cerebras Systems
  • CEVA
  • Esperanto Technologies
  • Facebook
  • Google
  • Graphcore
  • Groq
  • Gyrfalcon Technologies
  • Habana Labs (Intel)
  • Huawei
  • Intel
  • Kalray
  • MediaTek
  • Movidius (Intel)
  • Mobileye (Intel)
  • NVIDIA
  • Qualcomm
  • SambaNova
  • Thinci (now Blaize)
  • Xilinx

市场预测

  • 预测方法/前提条件
  • 总市场
  • 按芯片组类型划分的收入
  • 利润:通过学习和推理
  • 按计算能力计算的收入
  • 功耗消耗
  • 平均售价:按芯片组类型
  • 最终用户的收入
  • CPU
  • GPU
  • ASIC
  • FPGA
  • SoC加速器
  • 概述
目录
Product Code: DLC-20

The need for artificial intelligence (AI) acceleration is widely recognized as of 2020. AI acceleration chipsets have become a standard feature requirement for device manufacturers within the enterprise (data center) and edge markets. As a result, the volume and revenue of AI chipsets have increased drastically in the last two years. NVIDIA's latest A100 offers petaOPS of compute performance under certain compute conditions, marking a tremendous jump from the petaOPS server DGX-1 introduced just two short years ago.

Deep learning (DL) is slowly moving past its hype cycle as proof-of-concept (PoC) AI applications developed in the past two years go into production. AI chipset customers have become more sophisticated in terms of chipset needs for AI application acceleration and are asking for specific benchmarks when talking to vendors. Customers' needs for chipsets are coming to the forefront, forcing chipset companies to rethink the applicability of their technology. All prominent chip companies, such as Intel, NVIDIA, and Qualcomm, have invested heavily in AI. Cloud companies have started rolling out graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), giving developers a choice for AI acceleration. Omdia forecasts that global revenue for DL chipsets will increase from $11.4bn in 2019 to $71.2bn by 2025.

This Omdia Market Report assesses the industry dynamics, technology issues, and market opportunity surrounding DL chipsets, including CPUs, GPUs, FPGAs, ASICs, and SoC accelerators. As an update to Omdia's 2019 ‘Deep Learning Chipsets ’ report, it captures the state of this fast-moving chipset market. Global market forecasts, segmented by chipset type, compute capacity, power consumption, market sector, and training versus inference, extend through 2025. Omdia also provides profiles of 23 key industry players.

Key Questions Addressed:

  • What chipset types are being used for deep learning (DL) today, and how will they change through 2025 and beyond?
  • What are the power consumption and compute capacity profiles of chipsets used for DL applications?
  • What is the market opportunity for DL chipsets in enterprise environments versus edge devices?
  • Which market sectors and industries will drive demand for DL chipsets?
  • What is the state of technology development for DL chipsets, and which companies are driving innovation?
  • What are some of the emerging architectures for DL chipsets?
  • What are the key performance matrices for DL chipsets?
  • What are some of the use cases for DL chipsets in different application markets?
  • What has changed in the DL chipset market in the last two years?
  • How are startups faring in the DL chipset market?

Who Needs This Report?

  • Semiconductor and component manufacturers
  • OEM companies building devices using AI chipsets
  • Cloud companies using AI chipsets
  • Service providers and systems integrators
  • End-user organizations deploying deep learning systems
  • Industry associations
  • Government agencies
  • Investor community

Table of Contents

Executive summary

  • Introduction
  • 2020 report update
  • Key findings
  • Market forecasts

Market issues

  • Use of AI in the market
    • AI acceleration within enterprises
    • AI acceleration at the edge
  • Market segmentation
    • Segmentation by architecture (chipset type)
    • Segmentation based on training vs. inference
    • Segmentation based on compute capacity
    • Segmentation based on power consumption
    • Segmentation based on market sector: Enterprise and edge market
  • Market drivers
    • Popularity of AI and increasing complexity
    • Multiple AI pipelines
    • Complexity of training
    • Growth in enterprise applications
    • Desire to minimize production costs
    • Latency and throughput requirements for inference
    • Computer vision
    • Speech applications for embedded devices
  • Market barriers and challenges
    • Capital needs for chip development
    • Availability of expertise
    • Long development cycle and rapidly changing market
  • Applications and use cases
    • Enterprise applications and use cases
    • Edge applications and use cases
    • Other
  • Regional differences
  • Startup activity in deep learning chipsets
    • Many acquisitions
    • Casualties

Technology issues

  • Evolution of neural networks since 2012 and the need for hardware acceleration
    • Computation needs per forward pass (inference)
    • Compute needs for training
    • A neural network zoo
  • AI inference workloads
    • Recommendation engine
    • Image and video
    • Audio and speech
    • Text/natural language processing
    • Search
  • Translating neural network needs to chipset requirements
    • Processing elements and arithmetic logic units
    • Memory
    • On-chip connectivity
    • Chip-to-chip connectivity
  • Chipset architectures for deep learning
    • Central processing units
    • Graphics processing units
    • Field-programmable gate arrays
    • Application-specific integrated circuits
    • System-on-chip accelerators
  • Emerging AI acceleration architectures
    • Optical computing
    • Analog computing
    • Processing in memory
    • Neuromorphic
  • Technology parameters for chipsets
  • Benchmark
  • Data formats used in AI chipsets
  • Deep learning development frameworks

Key industry players

  • Amazon
  • AMD
  • ARM
  • Cerebras Systems
  • CEVA
  • Esperanto Technologies
  • Facebook
  • Google
  • Graphcore
  • Groq
  • Gyrfalcon Technologies
  • Habana Labs (acquired by Intel)
  • Huawei
  • Intel
  • Kalray
  • MediaTek
  • Movidius (Intel)
  • Mobileye (Intel)
  • NVIDIA
  • Qualcomm
  • SambaNova
  • Thinci (now Blaize)
  • Xilinx
  • Deep learning chipset and IP companies

Market forecasts

  • Forecast methodology and assumptions
    • Omdia coverage of AI chipsets
  • Overall market
  • Revenue by chipset type
  • Revenue by training vs. inference
  • Revenue by compute capacity
  • Revenue by power consumption
  • Average selling price by chipset type
  • Revenue by market sector
  • Central processing units
  • Graphics processing units
  • Application-specific integrated circuits
  • Field-programmable gate arrays
  • System-on-chip accelerators
  • Conclusions

Tables

  • Deep learning chipset revenue by chipset type, world markets: 2019-25
  • Deep learning chipset revenue, enterprise vs. edge, world markets: 2019-25
  • Deep learning chipset revenue growth rates, world markets: 2020-25
  • Deep learning chipset revenue by power consumption, world markets: 2019-25
  • Deep learning chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning chipset revenue, inference vs. training, world markets: 2019-25
  • Deep learning edge chipset revenue by chipset type, world markets: 2019-25
  • Deep learning edge chipset shipments by chipset type, world markets: 2019-25
  • Deep learning edge chipset revenue growth rates, world markets: 2020-25
  • Deep learning edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning edge chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning edge chipset revenue for inference vs. training, world markets: 2019-25
  • Deep learning enterprise chipset revenue by chipset type, world markets: 2019-25
  • Deep learning enterprise chipset shipments by chipset type, world markets: 2019-25
  • Deep learning enterprise chipset revenue growth rates, world markets: 2020-25
  • Deep learning enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning enterprise chipset revenue for inference vs. training, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, mobile, HMDs, drones, and machine vision (non-PC), world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, edge servers, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, PCs/tablets, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, cameras, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, smart speakers, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, automotive, world markets: 2019-25
  • Deep learning edge chipset ASPs by chipset type, robots, world markets: 2019-25
  • Deep learning enterprise training chipset ASPs by chipset type, world markets: 2019-25
  • Deep learning enterprise inference chipset ASPs by chipset type, world markets: 2019-25
  • Deep learning CPU chipset revenue by market sector, world markets: 2019-25
  • Deep learning CPU enterprise chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning CPU edge chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning CPU enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning CPU enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning CPU edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning CPU edge chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning GPU chipset revenue by market sector, world markets: 2019-25
  • Deep learning GPU enterprise chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning GPU edge chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning GPU enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning GPU enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning GPU edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning GPU edge chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning FPGA chipset revenue by market sector, world markets: 2019-25
  • Deep learning FPGA enterprise chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning FPGA edge chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning FPGA enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning FPGA enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning FPGA edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning FPGA edge chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning ASIC chipset revenue by market sector, world markets: 2019-25
  • Deep learning ASIC enterprise chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning ASIC edge chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning ASIC enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning ASIC enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning ASIC edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning ASIC edge chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning SoC accelerator chipset revenue by market sector, world markets: 2019-25
  • Deep learning SoC accelerator enterprise chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning SoC accelerator edge chipset revenue, training vs. inference, world markets: 2019-25
  • Deep learning SoC accelerator enterprise chipset revenue by power consumption, world markets: 2019-25
  • Deep learning SoC accelerator enterprise chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning SoC accelerator edge chipset revenue by power consumption, world markets: 2019-25
  • Deep learning SoC accelerator edge chipset revenue by compute capacity, world markets: 2019-25
  • Types of devices with enterprises using AI accelerators
  • Edge devices shipping in high volume and chipset requirements
  • Key players in different deep learning chipsets
  • Key CPU products and vendors
  • Key players in GPU
  • Key players in FPGA
  • Comparison of deep learning chipset parameters
  • Selected benchmarks for AI chipsets
  • Data formats used in AI chipsets
  • Popular deep learning frameworks
  • Deep learning chipset companies
  • IP companies

Figures

  • Deep learning chipset revenue, world markets: 2019-25
  • Estimated AI workloads on enterprise GPUs and CPUs
  • Deep learning chipset revenue, world markets: 2019-25
  • Deep learning chipset year-on-year revenue growth rates, world markets: 2020-25
  • Deep learning chipset revenue by chipset type, world markets: 2019-25
  • Deep learning chipset revenue, inference vs. training, world markets: 2019-25
  • Deep learning chipset revenue by compute capacity, world markets: 2019-25
  • Deep learning chipset revenue by power consumption, world markets: 2019-25
  • Deep learning chipset revenue by market sector, world markets: 2019-25
  • Deep learning CPU chipset revenue, world markets: 2019-25
  • Deep learning GPU chipset revenue, world markets: 2019-25
  • Deep learning ASIC chipset revenue, world markets: 2019-25
  • Deep learning FPGA chipset revenue, world markets: 2019-25
  • Deep learning SoC accelerator chipset revenue, world markets: 2019-25