市场调查报告书

云端AI晶片组:市场形势、供应商定位

Cloud AI Chipsets: Market Landscape and Vendor Positioning

出版商 ABI Research 商品编码 909244
出版日期 内容资讯 英文 30 Pages
商品交期: 最快1-2个工作天内
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云端AI晶片组:市场形势、供应商定位 Cloud AI Chipsets: Market Landscape and Vendor Positioning
出版日期: 2019年08月29日内容资讯: 英文 30 Pages
简介

云端AI晶片组的市场规模,估计2018年为35亿美元,预计2024年扩大到191亿美元。

本报告提供云端AI晶片组市场相关调查,AI及云端AI晶片组定义,主要的云端AI晶片组,市场趋势,市场预测,及主要企业的简介等系统性资讯。

第1章 摘要整理

第2章 人工智能 (AI)的定义

第3章 云端AI晶片组的演进

  • 通用AI硬体设备
  • FPGA的崛起
  • 专门工作负载的客制化晶片
  • 异质运算

第4章 云端AI晶片组定义

第5章 主要云端AI晶片组

  • 推论 (Inference) 用云端AI晶片
  • 训练用云端AI晶片

第6章 专属式供应商的崛起

第7章 主要供应商的简介

  • AWS
  • Baidu
  • Bitmain
  • Cambricon Technologies
  • Cerebras Systems
  • Google
  • Graphcore
  • Habana Labs
  • HiSilicon
  • Intel
  • NVIDIA
  • Qualcomm
  • Xilinx
  • Wave Computing

第8章 市场预测

  • 云端AI训练 vs. 推论
  • 云端AI晶片组的架构
  • 专属式供应商的崛起和对厂商市场占有率的影响

第9章 主要建议、结论

刊载企业

  • Alibaba
  • Amazon
  • Arteris IP
  • Baidu
  • Bitmain
  • Cambricon Technologies
  • Cerebras Systems
  • Dell
  • Facebook
  • Google
  • Graphcore
  • Groq
  • H3C
  • Habana Labs
  • HPE
  • Huawei
  • Inspur
  • Intel
  • Lenovo
  • Microsoft
  • NVIDIA
  • Qualcomm
  • Quanta
  • Rackscale
  • SambaNova Systems
  • Sugon
  • Supermicro
  • Tencent
  • Wave Computing
  • Xilinx
目录
Product Code: AN-5032

One of the key factors behind the rise of artificial intelligence (AI) is the upgrade in cloud computing power. This is largely driven by the enhancement and upgrade in cloud AI chipsets. Cloud AI chipsets are computational chipsets focusing on AI workload that is typical deployed in the cloud, or data center, environment. This chipset can be designed specifically for AI inference or AI training. In some instances, the chipset can support both AI inference and AI training.

Due to the constant evolution of AI algorithms, cloud AI chipsets are designed to support wide range of AI models, from rule-based AI to deep learning models, with varying degree of resource requirements. As compared to edge AI chipsets, a cloud AI chipset generally has higher computational power, higher power consumption, larger physical footprint and is therefore relatively more expensive.

Cloud AI market is so far dominated by NVIDIA GPUs and Intel's CPUs. In recent years, many companies have started to emerge and offer interesting take on how to address the challenge of AI workload in the cloud. On one hand, new startups like Cerebras Systems, Graphcore, Habana Labs, and Wave Computing have announced new chipsets that have higher performance or better computational flow as compared to conventional chipsets. On the other hand, captive vendors have started to build their own AI chips to power their data centers. Examples of these vendors include Amazon, Google, Huawei, Baidu and potentially Alibaba.

Overall, the market size for cloud AI chipsets is expected to be US$3.5 billion in 2018. This is expected to grow to US$19.1 billion in 2024. Right now, most of the market share is captured by non-captive vendors. As cloud service providers are going to take away majority of the AI workloads, we believe that their market share will grow from 2.3% in 2018 to 9.4% in 2024. For companies to be successful in this sector, the chipset must be highly scalable and flexible, achieve the right balance between performance and power budget, but also feature strong ecosystem support and comprehensive software stack.

Table of Contents

1. EXECUTIVE SUMMARY

2. DEFINITION OF ARTIFICIAL INTELLIGENCE

3. THE EVOLUTION OF CLOUD AI CHIPSET

  • 3.1. General-Purpose AI Hardware
  • 3.2. The Rise of the FPGA
  • 3.3. Custom Chips for Specific Workloads
  • 3.4. Heterogenous Computing

4. DEFINITIONS OF CLOUD AI CHIPSETS

5. MAJOR CLOUD AI CHIPSETS

  • 5.1. Cloud AI Chipsets for Inference
  • 5.2. Cloud AI Chipsets for Training

6. THE RISE OF CAPTIVE VENDORS

7. KEY VENDOR PROFILES

  • 7.1. AWS
  • 7.2. Baidu
  • 7.3. Bitmain
  • 7.4. Cambricon Technologies
  • 7.5. Cerebras Systems
  • 7.6. Google
  • 7.7. Graphcore
  • 7.8. Habana Labs
  • 7.9. HiSilicon
  • 7.10. Intel
  • 7.11. NVIDIA
  • 7.12. Qualcomm
  • 7.13. Xilinx
  • 7.14. Wave Computing

8. MARKET FORECASTS

  • 8.1. Cloud AI Training versus Inference
  • 8.2. Cloud AI Chipset Architecture
  • 8.3. The Rise of Captive Vendors and Their Impact on Vendor Share

9. KEY RECOMMENDATIONS AND CONCLUSIONS

Companies Mentioned

  • Alibaba
  • Amazon
  • Arteris IP
  • Baidu
  • Bitmain
  • Cambricon Technologies
  • Cerebras Systems
  • Dell
  • Facebook
  • Google
  • Graphcore
  • Groq
  • H3C
  • Habana Labs
  • HPE
  • Huawei
  • Inspur
  • Intel
  • Lenovo
  • Microsoft
  • NVIDIA
  • Qualcomm
  • Quanta
  • Rackscale
  • SambaNova Systems
  • Sugon
  • Supermicro
  • Tencent
  • Wave Computing
  • Xilinx
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