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市场调查报告书
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1721387

边缘AI市场 (～2035年):零组件·设备·数据类型·终端用户·各地区的产业趋势与全球预测

Edge AI Market, Till 2035: Distribution by Type of Component, Type of Device, Type of Data Type of End User, Type of Geographical Regions : Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 194 Pages | 商品交期: 2-10个工作天内

价格
简介目录

预计到 2035 年，全球边缘人工智慧市场规模将从目前的 240.5 亿美元增长至 3568.4 亿美元，预测期内的复合年增长率为 27.7%。

Edge AI Market-IMG1

边缘AI的市场机会:各市场区隔

各零件

  • 硬体设备
  • 软体
  • 服务

各设备

  • 边缘伺服器
  • 边缘闸道器
  • 边缘设备

按数据类型

  • 结构化资料
  • 非结构化资料

各终端用户

  • 汽车·运输
  • 能源·公共事业
  • 医疗保健
  • 製造
  • 零售
  • 其他

各地区

  • 北美
  • 美国
  • 加拿大
  • 墨西哥
  • 其他的北美各国
  • 欧洲
  • 奥地利
  • 比利时
  • 丹麦
  • 法国
  • 德国
  • 爱尔兰
  • 义大利
  • 荷兰
  • 挪威
  • 俄罗斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英国
  • 其他欧洲各国
  • 亚洲
  • 中国
  • 印度
  • 日本
  • 新加坡
  • 韩国
  • 其他亚洲各国
  • 南美
  • 巴西
  • 智利
  • 哥伦比亚
  • 委内瑞拉
  • 其他的南美各国
  • 中东·北非
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙乌地阿拉伯
  • UAE
  • 其他的中东·北非各国
  • 全球其他地区
  • 澳洲
  • 纽西兰
  • 其他的国家

边缘人工智慧市场：成长与趋势

随着物联网设备的不断扩展，对边缘人工智慧的需求也日益增长。边缘运算与人工智慧的结合可以提升运算能力，并使处理过程更接近设备，从而获取物联网设备和感测器生成的即时洞察。增强隐私和安全性是边缘人工智慧的关键因素，因为将敏感资料保存在设备上可以降低资料外洩的风险并保护隐私。

此外，5G 网路的持续部署将使边缘设备能够即时处理数据，从而扩展边缘人工智慧在远端手术、扩增实境 (AR) 和自动驾驶汽车等应用中的应用范围。边缘人工智慧也越来越多地应用于即时电脑视觉任务，例如製造现场的人脸辨识、物体侦测和品质侦测，这有望推动边缘人工智慧市场的成长。

整体而言，边缘人工智慧在各行各业的日益普及、智慧製造的进步以及自动驾驶汽车的广泛应用，预计将成为未来推动边缘人工智慧市场整体成长的主要因素。

本报告提供全球边缘AI的市场调查、 市场概要，背景，市场影响因素的分析，市场规模的转变·预测，各种区分·各地区的详细分析，竞争情形，主要企业简介等资讯。

目录

第1章 序文

第2章 调查手法

第3章 经济以及其他的计划特有的考虑事项

第4章 宏观经济指标

第5章 摘要整理

第6章 简介

第7章 竞争情形

第8章 企业简介

  • 章概要
  • Alphabet
  • Amazon Web Service
  • Apple
  • Arm Holding
  • Cisco
  • Dell Technological
  • Edge Impulse
  • Google
  • Gorilla Technology
  • Graphcore
  • Horizon Robotics
  • Huawei Technologies
  • IBM
  • Imagination Technologies
  • Intel
  • Microsoft
  • NVIDIA
  • Oracle
  • Qualcomm
  • Samsung
  • Siemens
  • Synaptics
  • Texas Instruments
  • Xilinx

第9章 价值链分析

第10章 SWOT分析

第11章 全球边缘AI市场

第12章 各零件的市场机会

第13章 各设备的市场机会

第14章 按数据类型的市场机会

第15章 各终端用户的市场机会

第16章 北美边缘AI的市场机会

第17章 欧洲的边缘AI的市场机会

第18章 亚洲的边缘AI的市场机会

第19章 中东与北非的边缘AI的市场机会

第20章 南美的边缘AI的市场机会

第21章 全球其他地区的边缘AI的市场机会

第22章 表格形式资料

第23章 企业·团体一览

第24章 客制化的机会

第25章 ROOTS的订阅服务

第26章 着者详细内容

简介目录
Product Code: RAICT300131

GLOBAL EDGE AI MARKET: OVERVIEW

As per Roots Analysis, the global edge AI market size is estimated to grow from USD 24.05 billion in the current year to USD 356.84 billion by 2035, at a CAGR of 27.7% during the forecast period, till 2035.

Edge AI Market - IMG1

The opportunity for edge AI market has been distributed across the following segments:

Type of Component

  • Hardware
  • Software
  • Services

Type of Device

  • Edge Servers
  • Edge Gateways
  • Edge Devices

Type of Data

  • Structured Data
  • Unstructured Data

Type of End User

  • Automotive & Transportation
  • Energy & Utilities
  • Healthcare
  • Manufacturing
  • Retail
  • Others

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

EDGE AI MARKET: GROWTH AND TRENDS

Edge AI, also known as AI at the edge, refers to the application of artificial intelligence in conjunction with edge computing. This approach enables data to be processed and analyzed at the location of generation, facilitating real-time decision-making with minimal delays. The prominent characteristics of edge AI include instantaneous processing, lower latency, and improved privacy and cyber security, making it a more attractive choice compared to cloud servers. A key benefit of edge AI is its ability to process information in milliseconds, providing real-time insights regardless of internet connectivity since artificial algorithms can analyze data near the device's location.

The growing expansion of IoT devices has driven the demand for edge AI, as the combination of edge computing and artificial intelligence enhances computational abilities and brings processing closer to where IoT devices and sensors generate real-time insights. Enhanced privacy and security are crucial components of edge AI since it keeps sensitive data on the device, reduces the likelihood of data breaches, and safeguards privacy.

Further, the ongoing rollout of the 5G network is expanding the capabilities of edge AI by enabling real-time data processing on edge devices, which is essential for applications such as remote surgeries, augmented reality, and self-driving vehicles. In addition, the increasing application of edge AI in real-time computer vision tasks, including facial recognition, object detection, and quality inspection in manufacturing, is projected to boost the growth of the edge AI market.

Overall, the significant adoption of edge AI across various industries, along with advancements in smart manufacturing and the rise of autonomous vehicles, are some of the primary factors that are likely to contribute in the overall growth of the edge AI market.

EDGE AI MARKET: KEY SEGMENTS

Market Share by Type of Component

Based on the type of component, the global edge AI market is segmented into hardware, software, and services. According to our estimates, currently, hardware component captures the majority share of the market. Edge chipsets, including graphic processing units, tensor processing units, field-programmable gate arrays, and application-specific integrated circuits possess significant processing power that manages the intensive computational tasks required by AI algorithms at the edge. As a result, these hardware components are essential for real-time data processing in IoT devices, thus aiding in the market's growth. However, software segment is anticipated to grow at a higher CAGR during the forecast period.

Market Share by Type of Device

Based on the type of device, the edge AI market is segmented into edge servers, edge gateways, and edge devices. According to our estimates, currently, edge devices like IoT devices, smartphones, and drones, captures the majority share of the market. Additionally, the growing use of AI-enabled IoT devices has driven demand within the market. The versatility and wide-ranging applications of edge devices, ranging from smart homes and wearable technology to smart transportation systems further fuels the growth in the market. However, edge servers segment is anticipated to grow at a higher CAGR during the forecast period.

Market Share by Type of Data

Based on the type of data, the edge AI market is segmented into structured data and unstructured data. According to our estimates, currently, unstructured data segment captures the majority share of the market, this segment is anticipated to grow at a higher CAGR in the future. The diverse origins of unstructured data, such as images, videos, text, and sensor data, along with the increasing demand for real-time information, strengthen the segment's position in the market.

Market Share by Type of End-User

Based on the type of end-user, the edge AI market is segmented into automotive & transportation, energy & utilities, healthcare, manufacturing, retail, and others. According to our estimates, currently, automotive and transportation industry captures the majority share of the market. This can be attributed to the increasing adoption of edge AI solutions in autonomous vehicles, which heavily depend on real-time data processing. As a result, the advantages of edge AI solutions in enhancing efficiency, improving safety, and reducing accidents and traffic congestion are believed to drive the growth of this segment. However, healthcare segment is anticipated to grow at a higher CAGR during the forecast period.

Market Share by Geographical Regions

Based on the geographical regions, the edge AI market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and Rest of the World. According to our estimates, currently, North America captures the majority share of the market. This can be attributed to the factors such as the early adoption of advanced technologies and the presence of tech companies in the region. However, market in Asia is anticipated to grow at a higher CAGR during the forecast period.

Example Players in Edge AI Market

  • Alphabet
  • Amazon Web Service
  • Apple
  • Arm Holding
  • Cisco
  • Dell Technology
  • Edge Impulse
  • Google
  • Gorilla Technology
  • Graphcore
  • Horizon Robotics
  • Huawei Technologies
  • IBM
  • Imagination Technologies
  • Intel
  • Microsoft
  • NVIDIA
  • Oracle
  • Qualcomm
  • Samsung
  • Siemens
  • Synaptics
  • Texas Instruments
  • Viso AI
  • Xilinx

EDGE AI MARKET: RESEARCH COVERAGE

The report on the edge AI market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the edge AI market, focusing on key market segments, including [A] type of component, [B] type of device, [C] type of data, [D] type of end use and [E] geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the Edge AI market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters, [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the Edge AI market, providing details on [A] location of headquarters, [B]company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] edge AI portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.

KEY QUESTIONS ANSWERED IN THIS REPORT

  • How many companies are currently engaged in edge AI market?
  • Which are the leading companies in this market?
  • What factors are likely to influence the evolution of this market?
  • What is the current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

REASONS TO BUY THIS REPORT

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

ADDITIONAL BENEFITS

  • Complimentary Excel Data Packs for all Analytical Modules in the Report
  • 10% Free Content Customization
  • Detailed Report Walkthrough Session with Research Team
  • Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. ECONOMIC AND OTHER PROJECT SPECIFIC CONSIDERATIONS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of Edge AI Market
    • 6.2.1. Type of Component
    • 6.2.2. Type of Device
    • 6.2.3. Type of Data
    • 6.2.4. Type of End User
  • 6.3. Future Perspective

7. COMPETITIVE LANDSCAPE

  • 7.1. Chapter Overview
  • 7.2. Edge AI: Overall Market Landscape
    • 7.2.1. Analysis by Year of Establishment
    • 7.2.2. Analysis by Company Size
    • 7.2.3. Analysis by Location of Headquarters
    • 7.2.4. Analysis by Ownership Structure

8. COMPANY PROFILES

  • 8.1. Chapter Overview
  • 8.2. Alphabet*
    • 8.2.1. Company Overview
    • 8.2.2. Company Mission
    • 8.2.3. Company Footprint
    • 8.2.4. Management Team
    • 8.2.5. Contact Details
    • 8.2.6. Financial Performance
    • 8.2.7. Operating Business Segments
    • 8.2.8. Service / Product Portfolio (project specific)
    • 8.2.9. MOAT Analysis
    • 8.2.10. Recent Developments and Future Outlook
  • 8.3. Amazon Web Service
  • 8.4. Apple
  • 8.5. Arm Holding
  • 8.6. Cisco
  • 8.7. Dell Technological
  • 8.8. Edge Impulse
  • 8.9. Google
  • 8.10. Gorilla Technology
  • 8.11. Graphcore
  • 8.12. Horizon Robotics
  • 8.13. Huawei Technologies
  • 8.14. IBM
  • 8.15. Imagination Technologies
  • 8.16. Intel
  • 8.17. Microsoft
  • 8.18. NVIDIA
  • 8.19. Oracle
  • 8.20. Qualcomm
  • 8.21. Samsung
  • 8.22. Siemens
  • 8.23. Synaptics
  • 8.24. Texas Instruments
  • 8.25. Xilinx

9. VALUE CHAIN ANALYSIS

10. SWOT ANALYSIS

11. GLOBAL EDGE AI MARKET

  • 11.1. Chapter Overview
  • 11.2. Key Assumptions and Methodology
  • 11.3. Trends Disruption Impacting Market
  • 11.4. Global Edge AI Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 11.5. Multivariate Scenario Analysis
    • 11.5.1. Conservative Scenario
    • 11.5.2. Optimistic Scenario
  • 11.6. Key Market Segmentations

12. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

  • 12.1. Chapter Overview
  • 12.2. Key Assumptions and Methodology
  • 12.3. Revenue Shift Analysis
  • 12.4. Market Movement Analysis
  • 12.5. Penetration-Growth (P-G) Matrix
  • 12.6. Edge AI Market for Hardware: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 12.7. Edge AI Market for Software: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 12.8. Edge AI Market for Services: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 12.9. Data Triangulation and Validation

13. MARKET OPPORTUNITIES BASED ON TYPE OF DEVICE

  • 13.1. Chapter Overview

132. Key Assumptions and Methodology

  • 13.3. Revenue Shift Analysis
  • 13.4. Market Movement Analysis
  • 13.5. Penetration-Growth (P-G) Matrix
  • 13.6. Edge AI Market for Edge Servers: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 13.7. Edge AI Market for Edge Gateways: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 13.8. Edge AI Market for Edge Devices: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 13.9. Data Triangulation and Validation

14. MARKET OPPORTUNITIES BASED ON TYPE OF DATA

  • 14.1. Chapter Overview
  • 14.2. Key Assumptions and Methodology
  • 14.3. Revenue Shift Analysis
  • 14.4. Market Movement Analysis
  • 14.5. Penetration-Growth (P-G) Matrix
  • 14.6. Edge AI Market for Structured Data: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 14.7. Edge AI Market for Unstructured Data: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 14.8. Data Triangulation and Validation

15. MARKET OPPORTUNITIES BASED ON TYPE OF END USER

  • 15.1. Chapter Overview
  • 15.2. Key Assumptions and Methodology
  • 15.3. Revenue Shift Analysis
  • 15.4. Market Movement Analysis
  • 15.5. Penetration-Growth (P-G) Matrix
  • 15.6. Edge AI Market for Automotive & Transportation: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.7. Edge AI Market for Energy & Utilities: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.8. Edge AI Market for Manufacturing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.9. Edge AI Market for Retail: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.10. Edge AI Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.11. Data Triangulation and Validation

16. MARKET OPPORTUNITIES FOR EDGE AI IN NORTH AMERICA

  • 16.1. Chapter Overview
  • 16.2. Key Assumptions and Methodology
  • 16.3. Revenue Shift Analysis
  • 16.4. Market Movement Analysis
  • 16.5. Penetration-Growth (P-G) Matrix
  • 16.6. Edge AI Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 16.6.1. Edge AI Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 16.6.2. Edge AI Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 16.6.3. Edge AI Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 16.6.4. Edge AI Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 16.7. Data Triangulation and Validation

17. MARKET OPPORTUNITIES FOR EDGE AI D IN EUROPE

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Revenue Shift Analysis
  • 17.4. Market Movement Analysis
  • 17.5. Penetration-Growth (P-G) Matrix
  • 17.6. Edge AI Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.1. Edge AI Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.2. Edge AI Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.3. Edge AI Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.4. Edge AI Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.5. Edge AI Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.6. Edge AI Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.7. Edge AI Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.8. Edge AI Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.9. Edge AI Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.10. Edge AI Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.11. Edge AI Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.12. Edge AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.13. Edge AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.14. Edge AI Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.15. Edge AI Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 17.6.16. Edge AI Marketing Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 17.7. Data Triangulation and Validation

18. MARKET OPPORTUNITIES FOR EDGE AI D IN ASIA

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Revenue Shift Analysis
  • 18.4. Market Movement Analysis
  • 18.5. Penetration-Growth (P-G) Matrix
  • 18.6. Edge AI Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.1. Edge AI Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.2. Edge AI Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.3. Edge AI Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.4. Edge AI Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.5. Edge AI Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 18.6.6. Edge AI Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.7. Data Triangulation and Validation

19. MARKET OPPORTUNITIES FOR EDGE AI IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. Edge AI Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.1. Edge AI Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
    • 19.6.2. Edge AI Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.3. Edge AI Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.4. Edge AI Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.5. Edge AI Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.6. Edge AI Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.7. Edge AI Market in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.6.8. Edge AI Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.7. Data Triangulation and Validation

20. MARKET OPPORTUNITIES FOR EDGE AI IN LATIN AMERICA

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. Edge AI Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.1. Edge AI Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.2. Edge AI Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.3. Edge AI Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.4. Edge AI Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.5. Edge AI Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 20.6.6. Edge AI Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.7. Data Triangulation and Validation

21. MARKET OPPORTUNITIES FOR EDGE AI IN REST OF THE WORLD

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. Edge AI Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 21.6.1. Edge AI Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 21.6.2. Edge AI Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 21.6.3. Edge AI Market in Other Countries
  • 21.7. Data Triangulation and Validation

22. TABULATED DATA

23. LIST OF COMPANIES AND ORGANIZATIONS

24. CUSTOMIZATION OPPORTUNITIES

25. ROOTS SUBSCRIPTION SERVICES

26. AUTHOR DETAIL