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IoT 人工智能邊緣運算

市場調查報告書

商品編碼

1446875

全球邊緣人工智慧市場

Global Edge AI Market

出版日期: 2024年02月13日 | 出版商:

DataM Intelligence | 英文 201 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

全球邊緣人工智慧市場在 2023 年達到 168 億美元，預計到 2031 年將達到 738 億美元，2024-2031 年預測期間CAGR為 20.6%。

透過邊緣運算技術（例如專用處理器和硬體加速器）的改進，邊緣的處理能力得到提高。由於處理能力增強，複雜的人工智慧模型可以在邊緣設備上有效地開發和運行，這使得困難的資料分析和即時推理工作更容易完成。

透過將電腦電源移近資料來源，邊緣運算可顯著降低資料的延遲和傳輸時間。對於需要即時做出決策的人工智慧應用程式，例如擴增實境、工業自動化和自動駕駛汽車，這種延遲的減少非常重要。邊緣運算透過減少延遲來提高系統效能和客戶滿意度，從而實現更快的人工智慧推理和回應時間。

主要參與者透過推出新產品在全球推廣邊緣人工智慧的措施日益增多，有助於推動預測期內全球邊緣人工智慧市場的成長。例如，2023年7月6日，Silicom與Hailo完成合作，推出Edge AI產品線。 Hailo 的 AI 加速器整合到 Silicom 目前的 Edge 平台中，解決了 Edge AI 應用程式的效能挑戰。因此，Silicom 的產品將以極具吸引力的性價比提供邊緣視覺處理和人工智慧推理。

北美組織和政府正在對邊緣人工智慧基礎設施、研發和研究進行戰略投資，以在全球市場上保持競爭力。由於商業投資、政府資助和公私合作等舉措，邊緣人工智慧產業正在不斷發展和創新。根據 5G Americas Omdia 進行的研究，截至 2023 年第三季度，北美地區擁有 1.76 億個 5G 連接，這意味著上一季度新增了 2,200 萬個連接。

動力學

物聯網 (IoT) 的日益普及

在網路邊界，感測器、攝影機和其他連接設備為物聯網設備提供大量資料。邊緣人工智慧無需依賴集中式雲端伺服器，可以直接在邊緣即時處理和分析這些資料，從而實現快速洞察和行動。許多物聯網用途（包括連網汽車、智慧家庭和工業自動化）都需要低延遲來實現即時回應。為了滿足這一需求，邊緣人工智慧在本地分析資料，從而降低延遲並確保快速決策，而不會因將資料發送到遠端資料中心而造成延遲。

過去 10 年，物聯網的使用量顯著增加。 IHS 預計，到 2022 年，使用的物聯網設備數量將增加近三倍，從 2015 年的 154.1 億增加到 426.2 億。預測表明，這一成長速度將會更快，預計到 2025 年，物聯網設備數量將達到 754.4 億台。推動物聯網成長的關鍵因素是不斷擴大的連接選項範圍。 5G 網路的可及性和寬頻速度的不斷提高推動了物聯網的發展，這使得設備能夠以迄今為止不可思議且高效的速度進行連接。

對自動駕駛汽車和機器人技術的需求不斷成長

即時處理來自少數感測器（例如攝影機、LiDAR、雷達和超音波感測器）的大量資訊對於機器人和獨立車輛至關重要。透過在組織邊緣本地處理訊息，邊緣模擬智慧使這些框架能夠快速做出選擇，並且減少對合併的雲端基礎的依賴。邊緣人工智慧使機器人和自動駕駛汽車能夠快速做出重要選擇，從而提高安全性和可靠性。該系統可以透過在邊緣立即處理資料來對不斷變化的環境條件和任何風險做出快速反應，從而降低發生事故的可能性並提高整體性能。

一些主要參與者遵循併購策略，這進一步有助於促進市場成長。例如，2020 年6 月19 日，自動駕駛汽車聯盟與邊緣運算潮流引領者凌華科技合作，共同利用邊緣人造智慧，為每個人提供獨立駕駛能力。凌華科技的整體願景是連接個人並積極影響商業和社會。。利用 Autoware 的開源自動駕駛技術，雙方將共同打造精明的交通和交通號誌系統。

資料隱私和安全問題

對資料安全和隱私的擔憂損害了客戶和企業對邊緣人工智慧系統收集、分析和保留資料的信心。缺乏信心會阻礙利益相關者共享敏感資料或在邊緣部署人工智慧應用程式，從而阻礙邊緣人工智慧解決方案的採用和資助。收集、處理和保護個人資料的組織必須遵守嚴格的限制。遵守這些法規會增加邊緣人工智慧實施的成本和複雜性，從而阻礙業務擴張。

隨著邊緣運算的發展以及世界上連網設備數量的不斷增加，邊緣人工智慧系統很容易遭受資料外洩、網路攻擊和未經授權的存取。其後果會降低消費者對邊緣人工智慧技術的信任並阻礙其擴張。有必要在邊緣人工智慧設定中整合強大的安全措施，如加密、存取限制、身份驗證系統和安全通訊協議，以確保資料隱私和安全。然而，企業發現很難在分散的邊緣設定中實施和維護這些安全控制，這阻礙了邊緣人工智慧解決方案的採用。

The processing power at the edge is increased by improvements in edge technology for computing, such as specialized processors and hardware accelerators. Complex AI models are developed and operated effectively on edge devices because of this enhanced processing power, which makes difficult data analysis and real-time inference jobs simpler to finish.

By moving computer power closer to the data source, edge computing dramatically lowers delay and transmission times for data. For AI applications that need to make decisions in real-time, such as augmented reality, industrial automation and autonomous vehicles, this latency reduction is important. Edge computing improves system performance and customer satisfaction by reducing latency, which allows for faster AI inference and response times.

The growing initiatives by the major key players to promote Edge AI globally by launching new products help to boost global edge AI market growth over the forecast period. For instance, on July 06, 2023, Silicom completed a partnership with Hailo to launch the Edge AI Product Line. The integration of Hailo's AI accelerators into Silicom's current Edge platforms addresses performance challenges for Edge AI applications. Consequently, Silicom's products will deliver visual processing and AI inference at the edge with an exceptionally appealing price/performance ratio.

North American organizations and governments are strategically investing in edge AI infrastructure, R&D and research to be competitive in the global market. The edge AI industry is growing and innovating because of initiatives including business investments, government funding and public-private partnerships. According to the study conducted by 5G Americas Omdia, North America leads with 176 million 5G connections as of Quarter 3 of 2023 which represents an additional 22 million new connections in the last quarter.

Dynamics

The Increasing Adoption of the Internet of Things (IoT)

At the border of the network, sensors, cameras and other connected devices offer enormous volumes of data for IoT devices. Without depending on centralized cloud servers, edge AI enables real-time processing and analysis of this data directly on the edge, enabling quick insights and actions. Low latency is required for real-time response for numerous Internet of Things uses, including linked cars, smart homes and industrial automation. To meet this need, Edge artificial intelligence analyses data locally, which lowers latency and ensures rapid decision-making without the delays imposed on by sending data to distant data centers.

IoT usage has risen significantly over the last 10 years. IHS anticipates that there will be nearly three times as many IoT devices used by 2022 from 15.41 billion in 2015 to 42.62 billion. Forecasts indicate that this increase will pick up even more velocity, with 75.44 billion IoT devices anticipated by 2025. A key element propelling the Internet of Things' growth is the ever-expanding range of connectivity options. IoT improvement has been energized by the rising accessibility of 5G organizations and broadband velocities, which enable devices to associate at rates that were up to this point unfathomable and productive.

Rising Demand for Autonomous Vehicles and Robotics

Real-time processing of enormous quantities of information from a few sensors, similar to cameras, lidar, radar and ultrasonic sensors, is essential for robots and independent vehicles. Through handling information locally at the organization's edge, edge-simulated intelligence empowers these frameworks to settle on choices rapidly and with less dependence on incorporated cloud foundations. Edge AI improves safety and dependability by empowering robotics and self-driving cars to make important choices quickly. The systems can react rapidly to shifting environmental conditions and any risks by processing data immediately at the edge, which lowers the possibility of accidents and boosts overall performance.

Some of the major key players follow merger and acquisition strategies which further help to boost market growth. For instance, on June 19, 2020, the Autonomous Vehicles Alliance and ADLINK, a trendsetter in edge computing with an overall vision to interface individuals and positively influence business and society, are cooperating to utilize edge man-made intelligence to empower independent driving for everybody. Using Autoware's open-source self-driving innovation, the participation will zero in on mutually fabricating astute transportation and traffic signal arrangements.

Data Privacy and Security Concerns

Concerns regarding data security and privacy damage customers' and businesses' faith in Edge AI systems to collect, analyze and retain their data. The lack of confidence prevents stakeholders from sharing sensitive data or deploying AI apps at the edge, which impedes the uptake and funding of Edge AI solutions. Organizations that collect, process and safeguard personal data have to abide by strict restrictions. Adherence to these regulations impedes business expansion by raising the costs and complexity of Edge AI implementations.

As edge computing grows and there is a growing number of connected gadgets in the world, edge AI systems are vulnerable to data breaches, cyberattacks and unauthorized access. The consequences reduce consumer trust in Edge AI technology and impede its expansion. It is necessary to integrate strong security measures, like encryption, access restrictions, authentication systems and secure communication protocols, in Edge AI settings to ensure data privacy and security. However, businesses find it difficult to implement and maintain these security controls across dispersed edge settings, which hinders the uptake of Edge AI solutions.

Segment Analysis

The global edge AI market is segmented based on component, technology, end-user and region.

Growing Adoption of Edge AI Software

Based on the components, the Edge AI market is segmented into Hardware, Software, Edge Cloud Infrastructure and Services. Software components in the market accounted largest market share due to the growing industrial adoption globally. Edge AI software solutions offer flexibility and adaptability to a wide range of edge computing devices and hardware platforms. The software solutions can be easily integrated into existing edge infrastructure, enabling organizations to leverage their investments in edge devices while adding AI capabilities. Edge AI software solutions can scale to meet the growing demands of diverse applications and use cases across industries. Organizations can deploy Edge AI software across multiple edge devices and locations, allowing for distributed processing and analysis of data without the need for significant hardware upgrades.

Globally, major key players launched innovative edge AI software which helps to boost segment growth over the forecast period. For instance, on February 26, 2024, Intel announced a new edge platform for scaling AI applications. The platform's edge infrastructure incorporates the OpenVINO AI inference runtime for edge AI, along with secure, policy-based automation of IT and OT management tasks. Over the past five years, Intel's OpenVINO has undergone evolution to assist developers in optimizing applications for low latency and low power consumption, facilitating deployment on existing hardware at the edge. The enables standard hardware that is already deployed to efficiently run AI applications without the need for costly upgrades or extensive modifications.

Geographical Penetration

North America is Dominating the Edge AI Market

North America is a pioneer in Edge AI technology development and adoption. Innovation and investment in Edge AI have been fueled by the region's strong ecosystem of technology startups, research centers and venture capitalists. Many significant technological companies that have led the way in creating and implementing Edge AI solutions are based in North America, including Google, Microsoft, Amazon, IBM and Intel. The businesses could dedicate substantial R&D resources to Edge AI research, development and commercialization.

Major key players in the region launched new innovative products which helped to boost regional market growth over the forecast period. For instance, on March 15, 2023, Texas Instruments launched a new family of six Arm Cortex-based vision processors that allow designers to add more vision and artificial intelligence (AI) processing at a lower cost and with better energy efficiency in various applications such as video doorbells, machine vision and autonomous mobile robots.

Competitive Landscape.

The major global players in the market include ADLINK Technology Inc., Alphabet Inc., Amazon.com, Inc., Gorilla Technology Group, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Nutanix, Inc. Synaptics Incorporated and Viso.ai.

COVID-19 Impact Analysis

Production and delivery of AI edge hardware components were impacted by the pandemic's interruption of global supply chains. Delays in the development and deployment of AI edge devices resulted from the availability of essential parts hampered by manufacturing slowdowns, movement restrictions and border closures. The epidemic pushed up the industry's adoption of digitalization and remote labor. To facilitate remote collaboration, improve cybersecurity for scattered networks and offer edge computing capabilities for distant operations, there's a greater need for AI edge solutions.

Due to the increase in digital activities and detached work, edge computing solutions were becoming increasingly vital to process data closer to the source and reduce latency. AI edge technologies are essential for providing edge computing capabilities, which increases approval in a variety of industries, including retail, logistics and manufacturing. The pandemic hampered research and development efforts in the AI edge sector, which led to several initiatives being shelved or delayed due to restricted access and collaboration in facilities. Research on AI-driven solutions for contact tracing, disease prediction at the edge and pandemic monitoring, however, has increased significantly.

Russia-Ukraine War Impact Analysis

The conflict disrupts the supply chains of AI edge technology components or manufacturing facilities located in the affected regions (Russia or Ukraine), it could lead to delays or shortages in product availability. It could impact companies reliant on these supply chains for their AI edge solutions. Geopolitical tensions create uncertainty in global markets, leading to hesitancy among businesses to invest in AI edge technologies due to concerns about geopolitical stability, trade disruptions or economic sanctions.

Companies increase their investment strategies in AI edge technologies, potentially diverting resources away from regions directly affected by the conflict to more stable areas. It could lead to shifts in research and development, manufacturing or investment in AI-edge startups and companies. Geopolitical tensions and conflicts prompt governments to enact new regulations or export controls on AI edge technologies, particularly if they are deemed sensitive or have dual-use applications. The regulatory changes could impact the global flow of AI edge technology and influence market dynamics.

By Component

Hardware
Software
Edge Cloud Infrastructure
Services

By Technology

Machine Learning (Deep Learning, Machine Learning Models)
Computer Vision
Natural Language Processing
Predictive Analytics

By End-User

Consumer Electronics
Manufacturing
Automotive
Government
Healthcare
Energy
Healthcare
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On February 01, 2024, Advantech launched a ruggedized edge AI system for heavy industry applications. The MIC-715-OX ruggedized edge AI system is purpose-built to address these exact challenges. Encased in ruggedized housing, it effectively mitigates vibrations and mechanical shocks. With an IP67 rating, it ensures resistance against water and dust ingress. Its fanless design ensures optimal cooling in any weather condition, eliminating the need to draw outside contaminated air into the enclosure.
On October 10, 2023, Macrometa launched PhotonIQ, AI Services at the Edge. PhotonIQ utilizes both Macrometa's unparalleled Global Data Network (GDN), which provides data and computational capabilities to two billion individuals and 10 billion devices globally and the most recent advancements in Artificial Intelligence and Machine Learning.
On January 10, 2024, Ambarella, Inc. launched the leading-edge Cooper Developer Platform. Cooper provides a seamless integration of software, hardware, advanced finely-tuned AI models and services, offering comprehensive support for Ambarella's complete range of AI systems-on-chip (SoCs).

Why Purchase the Report?

To visualize the global edge AI market segmentation based on component, technology, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of edge AI market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global edge AI market report would provide approximately 62 tables, 59 figures and 201 Pages.

Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Technology
3.3. Snippet by End-User
3.4. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. The Increasing Adoption of the Internet of Things (IoT)
  - 4.1.1.2. Rising Demand for Autonomous Vehicles and Robotics
- 4.1.2. Restraints
  - 4.1.2.1. Data Privacy and Security Concerns
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Component

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 7.1.2. Market Attractiveness Index, By Component
7.2. Hardware*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Software
7.4. Edge Cloud Infrastructure
7.5. Services

8. By Technology

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 8.1.2. Market Attractiveness Index, By Technology
8.2. Machine Learning (Deep Learning, Machine Learning Models) *
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Computer Vision
8.4. Natural Language Processing
8.5. Predictive Analytics

9. By End-User

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 9.1.2. Market Attractiveness Index, By End-User
9.2. Consumer Electronics*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Manufacturing
9.4. Automotive
9.5. Government
9.6. Healthcare
9.7. Energy
9.8. Healthcare
9.9. Others

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.6.1. U.S.
  - 10.2.6.2. Canada
  - 10.2.6.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.6.1. Germany
  - 10.3.6.2. UK
  - 10.3.6.3. France
  - 10.3.6.4. Italy
  - 10.3.6.5. Spain
  - 10.3.6.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.6.1. Brazil
  - 10.4.6.2. Argentina
  - 10.4.6.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.6.1. China
  - 10.5.6.2. India
  - 10.5.6.3. Japan
  - 10.5.6.4. Australia
  - 10.5.6.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

11.1. Competitive Scenario
11.2. Market Positioning/Share Analysis
11.3. Mergers and Acquisitions Analysis

12. Company Profiles

12.1. ADLINK Technology Inc.*
- 12.1.1. Company Overview
- 12.1.2. Product Portfolio and Description
- 12.1.3. Financial Overview
- 12.1.4. Key Developments
12.2. Alphabet Inc.
12.3. Amazon.com, Inc
12.4. Gorilla Technology Group
12.5. Intel Corporation
12.6. International Business Machines Corporation
12.7. Microsoft Corporation
12.8. Nutanix, Inc.
12.9. Synaptics Incorporated
12.10. Viso.ai

LIST NOT EXHAUSTIVE