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【分析レポート:アプリケーション】エッジAIチップセット:技術概観と利用ケース

Edge AI Chipsets: Technology Outlook and Use Cases

Application Analysis Report | 3Q 2019 | AN-4951

 

出版社 出版年月価格 ページ数
ABI Research
ABIリサーチ
2019年9月お問い合わせください 29

サマリー

As AI moves to the edge, edge AI chipsets becomes more important. Edge AI chipsets refers to computational chipsets focusing on AI workload that is typical deployed in edge environments, which include end devices, gateways and on-premise servers. This chipset is generally designed for AI inference workload, though in some cases, they can also support some level of AI training, particularly the training of deep learning models.

Overall, ABI Research estimates that the annual global edge AI chipset revenues for 2018 is US$10.6 billion. The market has experienced strong growth in the past and is expected to continue to grow to US$71 billion by 2024, with a CAGR of 31% between 2019 and 2024. Such strong growth is propelled by migration of AI inference workload to the edge, particularly in the smartphones, smart home, automotive, wearables, and robotics industry.

This report explores the dynamic landscape of edge AI landscape. By looking at chipset architecture, their respective computational requirements and use cases, the report provides a holistic view on the current state and future trends of edge AI chipset. Key players in the edge AI chipset industry have also been profiled with their key capabilities highlighted.

In addition, the report also looks into current development in open-source chipset. Under RISC-V, open-source chipset startups have started to develop AI-dedicated chipset with high parallelistic computing capabilities. Due to participation and contributions from across the industry, open-source AI chipsets will be more in line with market requirements and expectations, significantly reducing the cost of error and development costs in product maintenance and upgrade.

 

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(株式会社データリソース 03-3582-2531、office@dri.co.jp)

 

Companies Mentioned

  • AAEON
    Achronix
    ADLINK
    Adnes Technology
    AISpeech
    AlphaIC
    Ambarella
    Amlogic
    Apple
    ARM
    Baidu
    Bitmain
    Bragi
    Brain Corp.
    Broadcom
    C-Sky
    Cadence
    Cambricon Technologies
  • CEVA
    ChipIntelli
    DJI
    Ecovacs
    Efinix
    Esperanto Technolgies
    FANUC
    Google
    Greenwave Technologies
    Gyrfalcon Technology
    Hailo
    Hangzhou NationalChip
    Horizon Robotics
    Huawei
    iFlytek
    Imagination Technologies
    InCore Semiconductors
    Intel
  • Intuition Robotics
    iRobot
    Kneron
    Lattice Semiconductor
    LGE
    MediaTek
    Mobvoi
    Nissan
    NVIDIA
    NXP
    OPPO
    Qualcomm
    Quicklogic
    Renesas
    RISC-V
    Rockchip
    Samsung
  • SiFive
    Sonos
    Synopsys
    Syntiant
    TCL
    Tesla
    Unisound
    VeriSilicon
    videantis
    Vivo
    Volvo
    Wave Computing
    Whirlpool
    Xiaomi
    Xilinx
    Yamaha Motor
    Zenrin

 



目次

Table of Contents

  • 1. EXECUTIVE SUMMARY
  • 2. DEFINITION OF ARTIFICIAL INTELLIGENCE
  • 3. THE NEED FOR EDGE AI CHIPSETS
    • 3.1. AI Migration to the Edge
    • 3.2. Diversity and Complexity of Edge Use Cases
    • 3.3. List of Key AI Use Cases
  • 4. DEFINITIONS OF EDGE AI CHIPSETS
  • 5. KEY EDGE AI CHIPSET VENDORS
    • 5.1. IP Core Licensing Vendors
    • 5.2. Semiconductor Vendors
    • 5.3. Captive Vendors
  • 6. OPEN-SOURCE EDGE AI CHIPSETS
    • 6.1. Best Practice for Open-Source Chipsets
  • 7. THE EMERGENCE OF THE "VERY EDGE"
  • 8. MARKET FORECASTS
    • 8.1. Market Size
    • 8.2. Location of AI Inference and Training Workloads
    • 8.3. Revenue Forecasts
  • 9. KEY RECOMMENDATIONS AND CONCLUSIONS

 

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