首页 > 市场调查报告书 > 通讯

IoT 人工智能边缘运算

市场调查报告书

商品编码

1737223

全球边缘人工智慧硬体市场规模：按设备、处理器、消费量、最终用户、地区和预测

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个工作天内

价格

简介目录

边缘AI硬体的全球市场规模及预测

2024 年全球边缘 AI 硬体市场规模为 16.2 亿美元，预计到 2032 年将达到 72.2 亿美元，2026 年至 2032 年的复合年增长率为 20.46%。

边缘AI硬体是指具有人工智慧功能的运算设备，可以在数据生成点或附近处理数据，而不是依赖集中式云端伺服器。

该技术用于各种应用，包括智慧相机、自动驾驶汽车、工业自动化和物联网设备，实现即时数据处理、决策和提高生产力。

边缘 AI 硬体的未来一片光明，低延迟应用的需求不断增长、AI 演算法的突破以及医疗保健、零售和智慧城市等领域的广泛应用推动着功能越来越强大、节能的边缘设备的出现。

全球边缘人工智慧硬体市场动态

影响全球边缘人工智慧硬体市场的关键市场动态：

主要市场驱动因素：

即时人工智慧处理需求不断增长：各类应用对低延迟、即时人工智慧处理的需求正在推动边缘人工智慧技术的使用。根据国际数据公司 (IDC) 2024 年 2 月发布的分析报告，预计到 2025 年，包括边缘人工智慧硬体在内的全球边缘运算产业规模将达到 2,740 亿美元，2020 年至 2025 年的复合年增长率为 21.6%。报告也指出，到 2025 年，75% 的企业产生资料将在传统的集中式资料中心和云端之外建立和处理，高于 2018 年的 10%。无人驾驶汽车、智慧城市和工业IoT等产业对即时人工智慧应用的需求正在推动这种向边缘处理的转变。

物联网 (IoT) 生态系统蓬勃发展：物联网设备的快速普及推动了对能够在本地处理资料的边缘人工智慧 (AI) 技术的需求。根据 IoT Analytics 于 2024 年 1 月发布的报告，全球连网物联网装置的数量将从 2020 年的 117 亿大幅成长至 2025 年的 270 亿。分析显示，到 2025 年，超过一半的物联网设备将具备边缘人工智慧处理能力。物联网设备的激增正推动边缘人工智慧技术市场的显着成长，该技术用于管理边缘产生的大量数据。

日益增长的资料隐私和安全问题：资料隐私法规和日益增长的安全性担忧正在推动边缘人工智慧技术在本地资料处理中的应用。 2024年3月，欧盟网路安全局 (ENISA) 宣布，62% 的欧洲公司正在优先考虑边缘运算和本地资料处理，以遵守《一般资料保护规范》(GDPR) 等资料保护法规。根据同一项调查，与2024年云端基础的人工智慧解决方案相比，边缘人工智慧的引入使数据相关的安全问题减少了35%。

人工智慧晶片技术的进步：人工智慧晶片技术的快速发展使得边缘人工智慧设备更加强大、节能且经济高效。根据Gartner于2024年12月发布的产业预测，全球人工智慧晶片市场规模预计将从2024年的230亿美元成长到2027年的830亿美元，其中边缘人工智慧晶片将占该市场的40%。报告指出，自与前一年同期比较以来，边缘人工智慧处理器的每瓦效能平均年增35%。晶片技术的持续进步使得边缘人工智慧硬体更易于获取，并使其对从智慧型手机到工业设备等各种应用领域更具吸引力。

主要挑战

运算资源有限：与云端基础解决方案相比，边缘设备的处理能力、记忆体和能耗通常有限。这种限制使得运行需要大量处理资源的复杂 AI 模型和演算法变得困难，从而导致性能不佳或需要简化模型。

资料安全和隐私问题：边缘设备在本地处理敏感数据，因此实施强有力的安全措施至关重要。边缘AI硬体的漏洞可能导致资料外洩或未授权存取。组织需要创建严格的安全通讯协定来保护资料隐私，这可能会增加部署成本和复杂性。

互通性与标准化挑战：边缘AI生态系统包含不同製造商的硬体和软体平台。缺乏标准会使设备之间的整合和通讯变得困难，而这种互通性挑战会使系统设计、部署和维护变得复杂。

主要趋势：

物联网设备日益普及：物联网 (IoT) 设备的兴起推动了对边缘 AI 硬体的需求。为了正常运行，这些设备需要即时数据处理，并需要使用本地化计算资源来减少延迟并提高效能。

注重能源效率：随着企业环保意识的增强，节能型人工智慧设备也日益受到重视。边缘人工智慧系统旨在提供强大的处理能力，同时降低功耗，这对于长期运作至关重要，尤其是在偏远和资源匮乏的地区。

开发机器学习模型：

在边缘设备上运行更先进的机器学习模型是一个重要的趋势。模型压缩和量化等演算法创新使得复杂的人工智慧任务能够在资源受限的硬体上执行，从而扩展了边缘人工智慧在各行各业的应用范围。

增强安全性和隐私性：随着人们对资料隐私和网路安全的担忧日益加深，边缘人工智慧技术正在被赋予更强的安全措施。本地处理资料可以减少透过网路传输敏感资讯的需要，降低资料外洩的风险，并确保符合资料保护要求。

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

12 Appendix

12.1 Related Research

02-2729-4219

+886-2-2729-4219

License/价格

全球边缘人工智慧硬体市场规模：按设备、处理器、消费量、最终用户、地区和预测

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

边缘AI硬体的全球市场规模及预测

全球边缘人工智慧硬体市场动态

目录

第一章 全球边缘AI硬体市场介绍

第二章执行摘要

第三章：已验证的市场研究调查方法

第四章 全球边缘AI硬体市场展望

第五章 全球边缘人工智慧硬体市场（按设备）

第六章 全球边缘人工智慧硬体市场（按处理器）

第七章 全球边缘人工智慧硬体市场（按功耗）

第 8 章全球边缘 AI 硬体市场（按最终用户划分）

第九章 全球边缘人工智慧硬体市场（按地区）

第十章：全球边缘AI硬体市场竞争格局

第十一章 公司简介

第十二章 附录

Global Edge AI Hardware Market Size and Forecast

Global Edge AI Hardware Market Dynamics