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1721387

邊緣AI市場 (～2035年):零組件·設備·數據類型·終端用戶·各地區的產業趨勢與全球預測

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個工作天內

價格
簡介目錄

預計到 2035 年，全球邊緣人工智慧市場規模將從目前的 240.5 億美元增長至 3568.4 億美元，預測期內的複合年增長率為 27.7%。

Edge AI Market-IMG1

邊緣AI的市場機會:各市場區隔

各零件

  • 硬體設備
  • 軟體
  • 服務

各設備

  • 邊緣伺服器
  • 邊緣閘道器
  • 邊緣設備

按數據類型

  • 結構化資料
  • 非結構化資料

各終端用戶

  • 汽車·運輸
  • 能源·公共事業
  • 醫療保健
  • 製造
  • 零售
  • 其他

各地區

  • 北美
  • 美國
  • 加拿大
  • 墨西哥
  • 其他的北美各國
  • 歐洲
  • 奧地利
  • 比利時
  • 丹麥
  • 法國
  • 德國
  • 愛爾蘭
  • 義大利
  • 荷蘭
  • 挪威
  • 俄羅斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英國
  • 其他歐洲各國
  • 亞洲
  • 中國
  • 印度
  • 日本
  • 新加坡
  • 韓國
  • 其他亞洲各國
  • 南美
  • 巴西
  • 智利
  • 哥倫比亞
  • 委內瑞拉
  • 其他的南美各國
  • 中東·北非
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙烏地阿拉伯
  • UAE
  • 其他的中東·北非各國
  • 全球其他地區
  • 澳洲
  • 紐西蘭
  • 其他的國家

邊緣人工智慧市場：成長與趨勢

隨著物聯網設備的不斷擴展，對邊緣人工智慧的需求也日益增長。邊緣運算與人工智慧的結合可以提升運算能力，並使處理過程更接近設備，從而獲取物聯網設備和感測器生成的即時洞察。增強隱私和安全性是邊緣人工智慧的關鍵因素，因為將敏感資料保存在設備上可以降低資料外洩的風險並保護隱私。

此外，5G 網路的持續部署將使邊緣設備能夠即時處理數據，從而擴展邊緣人工智慧在遠端手術、擴增實境 (AR) 和自動駕駛汽車等應用中的應用範圍。邊緣人工智慧也越來越多地應用於即時電腦視覺任務，例如製造現場的人臉辨識、物體偵測和品質偵測，這有望推動邊緣人工智慧市場的成長。

整體而言，邊緣人工智慧在各行各業的日益普及、智慧製造的進步以及自動駕駛汽車的廣泛應用，預計將成為未來推動邊緣人工智慧市場整體成長的主要因素。

本報告提供全球邊緣AI的市場調查、 市場概要，背景，市場影響因素的分析，市場規模的轉變·預測，各種區分·各地區的詳細分析，競爭情形，主要企業簡介等資訊。

目錄

第1章 序文

第2章 調查手法

第3章 經濟以及其他的計劃特有的考慮事項

第4章 宏觀經濟指標

第5章 摘要整理

第6章 簡介

第7章 競爭情形

第8章 企業簡介

  • 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 價值鏈分析

第10章 SWOT分析

第11章 全球邊緣AI市場

第12章 各零件的市場機會

第13章 各設備的市場機會

第14章 按數據類型的市場機會

第15章 各終端用戶的市場機會

第16章 北美邊緣AI的市場機會

第17章 歐洲的邊緣AI的市場機會

第18章 亞洲的邊緣AI的市場機會

第19章 中東與北非的邊緣AI的市場機會

第20章 南美的邊緣AI的市場機會

第21章 全球其他地區的邊緣AI的市場機會

第22章 表格形式資料

第23章 企業·團體一覽

第24章 客制化的機會

第25章 ROOTS的訂閱服務

第26章 著者詳細內容

簡介目錄
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