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IoT 人工智能邊緣運算

市場調查報告書

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1737223

全球邊緣人工智慧硬體市場規模：按設備、處理器、消費量、最終用戶、地區和預測

Global Edge AI Hardware Market Size By Device (Cameras, Robots, Smart Phones), By Processors (GPU, CPU), By Consumption, By End-User (Consumer Electronics, Automotive, Government), By Geographic Scope and Forecast

出版日期: 2025年05月09日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

邊緣AI硬體的全球市場規模及預測

2024 年全球邊緣 AI 硬體市場規模為 16.2 億美元，預計到 2032 年將達到 72.2 億美元，2026 年至 2032 年的複合年成長率為 20.46%。

邊緣AI硬體是指具有人工智慧功能的運算設備，可以在數據生成點或附近處理數據，而不是依賴集中式雲端伺服器。

該技術用於各種應用，包括智慧相機、自動駕駛汽車、工業自動化和物聯網設備，實現即時數據處理、決策和提高生產力。

邊緣 AI 硬體的未來一片光明，低延遲應用的需求不斷成長、AI 演算法的突破以及醫療保健、零售和智慧城市等領域的廣泛應用推動著功能越來越強大、節能的邊緣設備的出現。

全球邊緣人工智慧硬體市場動態

影響全球邊緣人工智慧硬體市場的關鍵市場動態：

主要市場促進因素：

即時人工智慧處理需求不斷成長：各類應用對低延遲、即時人工智慧處理的需求正在推動邊緣人工智慧技術的使用。根據國際數據公司 (IDC) 2024 年 2 月發布的分析報告，預計到 2025 年，包括邊緣人工智慧硬體在內的全球邊緣運算產業規模將達到 2,740 億美元，2020 年至 2025 年的複合年成長率為 21.6%。報告也指出，到 2025 年，75% 的企業產生資料將在傳統的集中式資料中心和雲端之外建立和處理，高於 2018 年的 10%。無人駕駛汽車、智慧城市和工業IoT等產業對即時人工智慧應用的需求正在推動這種向邊緣處理的轉變。

物聯網 (IoT) 生態系統蓬勃發展：物聯網設備的快速普及推動了對能夠在本地處理資料的邊緣人工智慧 (AI) 技術的需求。根據 IoT Analytics 於 2024 年 1 月發布的報告，全球連網物聯網裝置的數量將從 2020 年的 117 億大幅成長至 2025 年的 270 億。分析顯示，到 2025 年，超過一半的物聯網設備將具備邊緣人工智慧處理能力。物聯網設備的激增正推動邊緣人工智慧技術市場的顯著成長，該技術用於管理邊緣產生的大量數據。

日益成長的資料隱私和安全問題：資料隱私法規和日益成長的安全性擔憂正在推動邊緣人工智慧技術在本地資料處理中的應用。 2024年3月，歐盟網路安全局 (ENISA) 宣布，62% 的歐洲公司正在優先考慮邊緣運算和本地資料處理，以遵守《一般資料保護規範》(GDPR) 等資料保護法規。根據同一項調查，與2024年雲端基礎的人工智慧解決方案相比，邊緣人工智慧的引入使數據相關的安全問題減少了35%。

人工智慧晶片技術的進步：人工智慧晶片技術的快速發展使得邊緣人工智慧設備更加強大、節能且經濟高效。根據Gartner於2024年12月發布的產業預測，全球人工智慧晶片市場規模預計將從2024年的230億美元成長到2027年的830億美元，其中邊緣人工智慧晶片將佔該市場的40%。報告指出，自與前一年同期比較以來，邊緣人工智慧處理器的每瓦效能平均年增35%。晶片技術的持續進步使得邊緣人工智慧硬體更易於獲取，並使其對從智慧型手機到工業設備等各種應用領域更具吸引力。

主要挑戰

運算資源有限：與雲端基礎解決方案相比，邊緣設備的處理能力、記憶體和能耗通常有限。這種限制使得運行需要大量處理資源的複雜 AI 模型和演算法變得困難，從而導致性能不佳或需要簡化模型。

資料安全和隱私問題：邊緣設備在本地處理敏感數據，因此實施強力的安全措施至關重要。邊緣AI硬體的漏洞可能導致資料外洩或未授權存取。組織需要創建嚴格的安全通訊協定來保護資料隱私，這可能會增加部署成本和複雜性。

互通性與標準化挑戰：邊緣AI生態系統包含不同製造商的硬體和軟體平台。缺乏標準會使設備之間的整合和通訊變得困難，而這種互通性挑戰會使系統設計、部署和維護變得複雜。

主要趨勢：

物聯網設備日益普及：物聯網 (IoT) 設備的興起推動了對邊緣 AI 硬體的需求。為了正常運行，這些設備需要即時數據處理，並需要使用本地化計算資源來減少延遲並提高效能。

注重能源效率：隨著企業環保意識的增強，節能型人工智慧設備也日益受到重視。邊緣人工智慧系統旨在提供強大的處理能力，同時降低功耗，這對於長期運作至關重要，尤其是在偏遠和資源匱乏的地區。

開發機器學習模型：

在邊緣設備上運行更先進的機器學習模型是一個重要的趨勢。模型壓縮和量化等演算法創新使得複雜的人工智慧任務能夠在資源受限的硬體上執行，從而擴展了邊緣人工智慧在各行各業的應用範圍。

增強安全性和隱私性：隨著人們對資料隱私和網路安全的擔憂日益加深，邊緣人工智慧技術正在被賦予更強的安全措施。本地處理資料可以減少透過網路傳輸敏感資訊的需要，降低資料外洩的風險，並確保符合資料保護要求。

Growing Demand for Real-Time AI Processing: The demand for low-latency, real-time AI processing in a variety of applications is driving the use of edge AI technology. In February 2024According to an International Data Corporation (IDC) analysis published, the global edge computing industry, which includes edge AI hardware, is predicted to reach USD 274 Billion by 2025, rising at a CAGR of 21.6% between 2020 and 2025. The paper states that by 2025, 75% of enterprise-generated data would be created and processed outside of a traditional centralized data center or cloud, up from 10% in 2018. The demand for real-time AI applications in industries such as driverless vehicles, smart cities, and industrial IoT is driving this transition to edge processing.

The growing Internet of Things (IoT) Ecosystem: The fast proliferation of IoT devices is increasing the demand for edge AI technology that can process data locally. In January 2024, According to an IoT Analytics report published, the number of linked IoT devices worldwide will reach 27 billion by 2025, up significantly from 11.7 billion in 2020. According to the analysis, by 2025, more than half of these gadgets will have edge AI processing capability. The growth of IoT devices has created a sizable market for edge AI technology to manage the large amounts of data generated at the edge.

Rising Concerns over Data Privacy and Security: Increasing data privacy rules and security concerns are encouraging the use of edge AI technology for local data processing. In March 2024, the European Union Agency for Cybersecurity (ENISA) announced that 62% of European firms are emphasizing edge computing and local data processing to comply with data protection rules such as GDPR. According to the survey, edge AI deployments reduced data-related security problems by 35% when compared to cloud-based AI solutions in 2024.

Advancements in AI Chip Technology: Rapid advancements in AI chip technology make edge AI gear more powerful, energy-efficient, and cost-effective. In December 2024Gartner's industry estimate, published, shows that the worldwide AI chip market is predicted to increase from USD 23 Billion in 2024 to USD 83 Billion by 2027, with edge AI chips accounting for 40% of this market. According to the paper, the performance per watt of edge AI processors has increased by an average of 35% year on year since 2020. This ongoing progress in chip technology makes edge AI hardware more accessible and appealing for a wide range of applications, from smartphones to industrial equipment.

Key Challenges:

Limited Computing Resources: When compared to cloud-based solutions, edge devices frequently have limited processing power, memory, and energy. This constraint makes it difficult to run complicated AI models and algorithms that need large processing resources, potentially resulting in suboptimal performance or the need for model simplification.

Data Security and Privacy Concerns: Edge devices handle sensitive data locally, implementing strong security measures is critical. Vulnerabilities in edge AI hardware might result in data breaches and unauthorized access. Organizations must create strict security protocols to preserve data privacy, which can raise costs and complicate deployment.

Interoperability and Standardization Issues: The Edge AI ecosystem includes a variety of hardware and software platforms from different manufacturers. Lack of standards might make it difficult to integrate and communicate amongst devices this interoperability difficulty may result in greater complexity in system design, deployment, and maintenance.

Key Trends:

Increasing Adoption of IoT Devices: The growth of Internet of Things (IoT) devices is boosting demand for edge AI hardware. To function properly, these devices require real-time data processing, which necessitates the use of localized computing resources to reduce latency and improve performance.

Focus on Energy Efficiency: As corporations become more eco-conscious, there is a greater emphasis on energy-efficient AI gear. Edge AI systems are being built to use less power while yet providing high processing capabilities, which is critical for long-term operations, particularly in distant or resource-constrained locations.

Developments in Machine Learning Models: The development of more advanced machine learning models that can operate on edge devices is an important trend. Algorithmic innovations, such as model compression and quantization, enable complicated AI tasks will be conducted on hardware with limited resources, broadening the applications of edge AI across industries.

Enhancing Security and Privacy: With growing worries about data privacy and cybersecurity, edge AI technology is being created with stronger security measures. Processing data locally lowers the need to transmit sensitive information over the internet, lowering the danger of data breaches and guaranteeing compliance with data protection requirements.

Global Edge AI Hardware Market Regional Analysis

Here is a more detailed regional analysis of the global edge AI hardware market:

North America:

The North American area currently dominates the Edge AI hardware market, owing to rapid technological breakthroughs and a strong presence of major businesses. The region benefits from major expenditures in AI research and development, supported by tech behemoths like NVIDIA, Intel, and Microsoft. In August 2024, NVIDIA introduced a new line of edge AI hardware aimed at optimizing real-time processing for autonomous vehicles and smart manufacturing, demonstrating its commitment to improving edge computing capabilities.

Government initiatives, such as financing for AI research in various areas, help to fuel this progress. In July 2024, the US government dedicated USD 500 Million to promote edge AI development initiatives as part of a larger strategy to improve national cybersecurity and data processing efficiency. Furthermore, the growing demand for low-latency processing in industries like healthcare, automotive, and retail is driving investment in edge AI solutions. The partnership between business and public sector projects is projected to result in a solid ecosystem for Edge AI hardware, cementing North America's dominant position in this quickly changing market.

Asia Pacific:

The Asia Pacific area is emerging as the fastest-growing market for edge AI hardware, driven by increased expenditures in digital transformation and rising need for real-time data processing across a wide range of industries. Countries such as China, India, and Japan are driving this expansion through considerable advances in 5G technology and IoT infrastructure. In September 2024, Alibaba Cloud announced the debut of its new edge AI platform focused at improving smart city applications and autonomous systems, reflecting the region's emphasis on combining edge computing with AI technology.

In July 2024, South Korea's Ministry of Science and ICT announced a USD 250 Million investment in edge computing infrastructure to assist smart manufacturing and self-driving vehicles. These government-led initiatives, combined with significant private sector expenditures, are driving the Asia Pacific Edge AI hardware market to new heights.

Global Edge AI Hardware Market: Segmentation Analysis

The Global Edge AI Hardware Market is segmented on the basis of By Device, By Processors, By Consumption, By End-User and Geography.

Global Edge AI Hardware Market, By Device

Cameras
Robots
Smart Phones

Based on Device, the Global Edge AI Hardware Market is segmented into Cameras, Robots, and Smart Phones. Smartphones are the leading segment, thanks to the incorporation of AI capabilities for better user experiences, such as photography, virtual assistants, and personalized services. However, the robotics market is the fastest-growing, thanks to increased investments in automation and AI-driven solutions in industries such as manufacturing, logistics, and healthcare, where robots are used for activities that demand real-time decision-making and adaptability.

Global Edge AI Hardware Market, By Processors

Based on Processors, the Global Edge AI Hardware Market is segmented into GPU and CPU. The GPU segment dominates because to its greater parallel processing capabilities, making it perfect for tackling sophisticated AI workloads like image recognition and deep learning activities in edge devices. However, the CPU segment is the fastest expanding, thanks to developments in AI-optimized CPUs that provide greater efficiency and performance for AI inference jobs in energy-constrained situations such as IoT devices and edge computing applications.

Global Edge AI Hardware Market, By Consumption

Less than 1\W

1-3W

3-5 W

Based on Consumption, the Global Edge AI Hardware market is segmented into less than 1\W, 1-3W, and 3-5 W. The 1-3W sector is dominating due to its mix of power economy and performance, making it suitable for a wide range of AI applications in consumer electronics and IoT devices. However, the less than 1W category is the fastest expanding, owing to rising demand for ultra-low-power AI chips in wearable devices, smart sensors, and edge devices that require little power consumption while retaining AI capabilities.

Global Edge AI Hardware Market, By End-User

Consumer Electronics
Automotive
Government

Based on End-User, the Global Edge AI Hardware market is segmented into Consumer Electronics, Automotive, and Government. The consumer electronics market is dominant, owing to the extensive usage of AI-powered gadgets such as smartphones, wearables, and smart home goods, especially in North America and Europe. However, the automotive market is the fastest-growing, because to the rapid integration of AI in self-driving cars, advanced driver assistance systems (ADAS), and smart mobility solutions, particularly in Asia Pacific.

Global Edge AI Hardware Market, By Geography

North America
Europe
Asia Pacific
Rest of the World
On the basis of Geography, the Global Edge AI Hardware Market are classified into North America, Europe, Asia Pacific, and Rest of World. North America is the dominant region due to its high concentration of technological giants, advanced infrastructure, and early adoption of AI-driven solutions in industries such as automotive, healthcare, and consumer electronics. However, Asia Pacific is the fastest-growing region, owing to rapid industrialization, increased investments in AI technology, and rising demand for smart devices and automation in nations such as China, Japan, and South Korea.

Key Players

The "Global Edge AI Hardware Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are IBM, Microsoft, Google, NVIDIA, Intel, Samsung, Huawei, Media Tek, Inc., Imagination Technologies, and Xilinx, Inc.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with its product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Global Edge AI Hardware Market: Recent Developments
In September 2024, Google Cloud unveiled new edge AI tools that enable real-time data processing and analytics in areas such as retail and healthcare. These solutions are intended to simplify operations and improve customer experiences by allowing organizations to make data-driven decisions rapidly.
In July 2024, Microsoft extended its Azure Stack Edge service with improved AI capabilities for local data processing. This extension seeks to enable organizations to run AI models at the edge, boosting response times and decreasing the need to send data back to the cloud for processing.

1 INTRODUCTION OF GLOBAL EDGE AI HARDWARE MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL EDGE AI HARDWARE MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL EDGE AI HARDWARE MARKET, BY DEVICE

5.1 Overview
5.2 Cameras
5.3 Robots
5.4 Smart Phones

6 GLOBAL EDGE AI HARDWARE MARKET, BY PROCESSORS

6.1 Overview
6.2 GPU
6.3 CPU

7 GLOBAL EDGE AI HARDWARE MARKET, BY POWER CONSUMPTION

7.1 Overview
7.2 Less than1 W
7.31-3 W
7.4 3-5 W
7.5 510W
7.610 W

8 GLOBAL EDGE AI HARDWARE MARKET, BY END USER

8.1 Overview
8.2 Consumer Electronics
8.3 Automotive
8.4 Government
8.5 Others

9 GLOBAL EDGE AI HARDWARE MARKET, BY GEOGRAPHY

9.1 Overview
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
- 9.2.3 Mexico
9.3 Europe
- 9.3.1 Germany
- 9.3.2 U.K.
- 9.3.3 France
- 9.3.4 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 Japan
- 9.4.3 India
- 9.4.4 Rest of Asia Pacific
9.5 Rest of the World
- 9.5.1 Latin America
- 9.5.2 Middle East & Africa

10 GLOBAL EDGE AI HARDWARE MARKET COMPETITIVE LANDSCAPE

10.1 Overview
10.2 Company Market Ranking
10.3 Key Development Strategies

11 COMPANY PROFILES

11.1 IBM
- 11.1.1 Overview
- 11.1.2 Financial Performance
- 11.1.3 Product Outlook
- 11.1.4 Key Developments
11.2 Microsoft
- 11.2.1 Overview
- 11.2.2 Financial Performance
- 11.2.3 Product Outlook
- 11.2.4 Key Developments
11.3 Google Inc.
- 11.3.1 Overview
- 11.3.2 Financial Performance
- 11.3.3 Product Outlook
- 11.3.4 Key Developments
11.4 NVIDIA
- 11.4.1 Overview
- 11.4.2 Financial Performance
- 11.4.3 Product Outlook
- 11.4.4 Key Developments
11.5 Intel
- 11.5.1 Overview
- 11.5.2 Financial Performance
- 11.5.3 Product Outlook
- 11.5.4 Key Developments
11.6 Samsung
- 11.6.1 Overview
- 11.6.2 Financial Performance
- 11.6.3 Product Outlook
- 11.6.4 Key Developments
11.7 Huawei
- 11.7.1 Overview
- 11.7.2 Financial Performance
- 11.7.3 Product Outlook
- 11.7.4 Key Developments
11.8 Media Tek Inc.
- 11.8.1 Overview
- 11.8.2 Financial Performance
- 11.8.3 Product Outlook
- 11.8.4 Key Developments
11.9 Imagination Technologies
- 11.9.1 Overview
- 11.9.2 Financial Performance
- 11.9.3 Product Outlook
- 11.9.4 Key Developments
11.10 Xilinx Inc.
- 11.10.1 Overview
- 11.10.2 Financial Performance
- 11.10.3 Product Outlook
- 11.10.4 Key Developments