市場調查報告書

雲端AI晶片組:市場形勢、供應商定位

Cloud AI Chipsets: Market Landscape and Vendor Positioning

出版商 ABI Research 商品編碼 909244
出版日期 內容資訊 英文 30 Pages
商品交期: 最快1-2個工作天內
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雲端AI晶片組:市場形勢、供應商定位 Cloud AI Chipsets: Market Landscape and Vendor Positioning
出版日期: 2019年08月29日內容資訊: 英文 30 Pages
簡介

雲端AI晶片組的市場規模,估計2018年為35億美元,預計2024年擴大到191億美元。

本報告提供雲端AI晶片組市場相關調查,AI及雲端AI晶片組定義,主要的雲端AI晶片組,市場趨勢,市場預測,及主要企業的簡介等系統性資訊。

第1章 摘要整理

第2章 人工智能 (AI)的定義

第3章 雲端AI晶片組的演進

  • 通用AI硬體設備
  • FPGA的崛起
  • 專門工作負載的客製化晶片
  • 異質運算

第4章 雲端AI晶片組定義

第5章 主要雲端AI晶片組

  • 推論 (Inference) 用雲端AI晶片
  • 訓練用雲端AI晶片

第6章 專屬式供應商的崛起

第7章 主要供應商的簡介

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

第8章 市場預測

  • 雲端AI訓練 vs. 推論
  • 雲端AI晶片組的架構
  • 專屬式供應商的崛起和對廠商市場佔有率的影響

第9章 主要建議、結論

刊載企業

  • 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
目錄
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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