查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
面向物联网应用的设备端人工智慧市场—第一版
市场调查报告书
商品编码
1871067

面向物联网应用的设备端人工智慧市场—第一版

The On-device AI Market for IoT Applications - 1st Edition
出版日期: | 出版商: Berg Insight | 英文 90 Pages | 商品交期: 最快1-2个工作天内

价格

预计到2024年，设备端人工智慧解决方案的营收将达到101亿美元，年增22%。预计到 2029 年，市场规模将达到 306 亿美元，复合年增长率 (CAGR) 为 25%。

本报告分析了物联网应用的设备端人工智慧市场，并提供了来自主要公司高管访谈的洞察、到 2029 年的市场规模预测以及领先供应商的市场占有率和概况。

目录

图表清单

摘要整理

第一章：引言

  • 云端处理与装置端处理
    • 设备端处理
    • 云端处理
    • 边缘资料中心处理
    • 混合法
  • 人工智慧物联网：人工智慧与物联网的融合
    • 物联网设备的构成要素
    • 物联网连线选项
  • 人工智慧技术概述
    • 人工智慧
    • 机器学习
    • 深度学习
    • 生成式人工智慧
  • 设备端人工智慧生态系统
    • 装置端人工智慧硬体
    • 装置端人工智慧软体
    • 设备端人工智慧模型
    • 装置端人工智慧平台

第二章 市场分析

  • 设备端人工智慧产业概况
    • 人工智慧SoC/SoM供应商
    • 人工智慧加速器供应商
    • 人工智慧MCU供应商
    • 设备端人工智慧平台供应商
  • 市场规模估算与预测
    • 汽车设备端人工智慧市场规模
    • 物联网设备端人工智慧市场规模
    • 装置端生成式/非生成式人工智慧市场规模
    • 装置端人工智慧处理器出货量
  • 解决方案提供商市场占有率
  • 各行业的采用情况与应用案例
    • 汽车物联网应用中的装置端人工智慧
    • 工业物联网应用中的设备端人工智慧
    • 穿戴式装置中的装置端人工智慧
    • 零售物联网应用中的装置端人工智慧
    • 楼宇与安防物联网应用中的设备端人工智慧
    • 智慧家庭应用中的装置端人工智慧
    • 其他物联网应用中的装置端人工智慧

第3章 企业的简介与策略

  • Ambarella
  • Ambiq
  • Advanced Micro Devices (AMD)
  • Apple
  • Axelera
  • Black Sesame Technologies
  • DEEPX
  • EdgeCortix
  • Edge Impulse
  • EmbedUR
  • Hailo
  • Horizon Robotics
  • Hugging Face
  • Intel
  • MediaTek
  • MemryX
  • Mobileye
  • Mythic
  • Nota AI
  • NVIDIA
  • NXP Semiconductors
  • Qualcomm
  • Renesas Electronics
  • Rockchip
  • SigmaStar
  • SiMa
  • STMicroelectronics
  • Synaptics
  • Syntiant
  • Tesla
  • Texas Instruments
  • 缩写和简称的清单

Berg Insight estimates that the revenues generated by on-device AI solutions reached US$ 10.1 billion in 2024, an increase of 22 percent year-on-year. This figure includes AI SoCs/SoMs, AI accelerators, AI MCUs and specialised on-device AI software and platforms, but excludes revenues generated by non-IoT applications such as smartphones, tablets and personal computers. The market is expected to grow to US$ 30.6 billion in 2029, representing a CAGR of 25 percent. Get up to date with the latest trends and developments with this unique 90-page report.

Highlights from the report:

  • Insights from numerous executive interviews with market leading companies.
  • 360-degree overview of the on-device AI ecosystem.
  • Market value forecast for on-device AI hardware and software until 2029.
  • Market shares for 40 key on-device AI hardware and software providers.
  • Detailed profiles of 31 key on-device AI hardware and software providers.
  • Use case descriptions across the most important industry verticals.
  • In-depth analysis of market trends and key developments.

Table of Contents

Table of Contents

List of Figures

Executive Summary

1. Introduction

  • 1.1. Cloud vs on-device processing
    • 1.1.1. On-device processing
    • 1.1.2. Cloud processing
    • 1.1.3. Edge data centre processing
    • 1.1.4. Hybrid approaches
  • 1.2. AIoT: The convergence of AI and IoT
    • 1.2.1. What constitutes an IoT device?
    • 1.2.2. IoT connectivity options
  • 1.3. Artificial intelligence technology overview
    • 1.3.1. Artificial intelligence
    • 1.3.2. Machine learning
    • 1.3.3. Deep learning
    • 1.3.4. Generative AI
  • 1.4. On-device AI ecosystem
    • 1.4.1. On-device AI hardware
    • 1.4.2. On-device AI software
    • 1.4.3. On-device AI models
    • 1.4.4. On-device AI platforms

2. Market Analysis

  • 2.1. The on-device AI industry landscape
    • 2.1.1. AI SoC/SoM providers
    • 2.1.2. AI accelerator providers
    • 2.1.3. AI MCU providers
    • 2.1.4. On-device AI platform providers
  • 2.2. Market sizing and forecast
    • 2.2.1. Automotive on-device AI market size
    • 2.2.2. IoT on-device AI market size
    • 2.2.3. On-device GenAI vs non-GenAI market size
    • 2.2.4. On-device AI processor shipments
  • 2.3. Solution provider market shares
  • 2.4. Vertical adoption and use cases
    • 2.4.1. On-device AI in automotive IoT applications
    • 2.4.2. On-device AI in industrial IoT applications
    • 2.4.3. On-device AI in wearables
    • 2.4.4. On-device AI in retail IoT applications
    • 2.4.5. On-device AI in buildings & security IoT applications
    • 2.4.6. On-device AI in smart home applications
    • 2.4.7. On-device AI in other IoT applications

3. Company Profiles and Strategies

  • 3.1. Ambarella
  • 3.2. Ambiq
  • 3.3. Advanced Micro Devices (AMD)
  • 3.4. Apple
  • 3.5. Axelera
  • 3.6. Black Sesame Technologies
  • 3.7. DEEPX
  • 3.8. EdgeCortix
  • 3.9. Edge Impulse
  • 3.10. EmbedUR
  • 3.11. Hailo
  • 3.12. Horizon Robotics
  • 3.13. Hugging Face
  • 3.14. Intel
  • 3.15. MediaTek
  • 3.16. MemryX
  • 3.17. Mobileye
  • 3.18. Mythic
  • 3.19. Nota AI
  • 3.20. NVIDIA
  • 3.21. NXP Semiconductors
  • 3.22. Qualcomm
  • 3.23. Renesas Electronics
  • 3.24. Rockchip
  • 3.25. SigmaStar
  • 3.26. SiMa
  • 3.27. STMicroelectronics
  • 3.28. Synaptics
  • 3.29. Syntiant
  • 3.30. Tesla
  • 3.31. Texas Instruments
  • List of Acronyms and Abbreviations

List of Figures

  • Figure 1.1: Comparison of on-device, edge data centre and cloud processing
  • Figure 1.2: Comparison between wireless technologies
  • Figure 1.3: The relationship between AI terminologies
  • Figure 1.4: Neural network illustration
  • Figure 1.5: Examples of an AI SoC (left) and SoM (right)
  • Figure 1.6: Typical deployment methodology
  • Figure 2.1: Core business activities of key on-device AI vendors
  • Figure 2.2: On-device AI revenues for IoT applications (World 2023-2029)
  • Figure 2.3: On-device AI revenues for IoT applications by type (2024 vs 2029)
  • Figure 2.4: On-device AI processor shipments (World 2023-2029)
  • Figure 2.5: On-device AI market shares (World 2024)
  • Figure 2.6: On-device AI revenues by vertical (World 2024)
  • Figure 2.7: Sales of passenger cars by SAE autonomy level (World 2024-2030)
  • Figure 2.8: On-device AI enabling autonomous retail at Amazon Go
  • Figure 3.1: Example of Ambiq's Apollo SoC integration in a smartwatch
  • Figure 3.2: AMD revenues by segment (2023-2024)
  • Figure 3.3: Apple's annual revenues by product category (2023-2025)
  • Figure 3.4: DEEPX's DX-M1 M.2 module
  • Figure 3.5: EdgeCortix's software stack
  • Figure 3.6: Edge Impulse workflow example
  • Figure 3.7: Hailo's stack and deployment methodology
  • Figure 3.8: Intel revenues by segment (2022-2024)
  • Figure 3.9: Intel Core Ultra 200U and 200H processors
  • Figure 3.10: Overview of Mobileye ADAS and autonomous vehicle platforms (Q1-2025)
  • Figure 3.11: Mythic's AI deployment workflow
  • Figure 3.12: NVIDIA hardware portfolio for on-device AI processing
  • Figure 3.13: Nvidia Jetson platform software stack
  • Figure 3.14: NVIDIA's Automotive & Robotics revenues
  • Figure 3.15: Qualcomm's Snapdragon Ride AD solutions (April 2025)
  • Figure 3.16: QCT revenues by segment (2023-2025)
  • Figure 3.17: Renesas AI vision processor portfolio (March 2025)
  • Figure 3.18: SigmaStar AI display SoCs
  • Figure 3.19: SiMa MLSoC Modalix specifications
  • Figure 3.20: STMicroelectronics' STM32N6 portfolio
  • Figure 3.21: Syntiant's 1.4mm x 1.8mm NDP100 processor
  • Figure 3.22: Tesla Hardware 3 module
  • Figure 3.23: Texas Instruments' edge AI portfolio