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IoT 人工智能边缘运算

市场调查报告书

商品编码

1446875

全球边缘人工智慧市场

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: 约2个工作天内

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简介目录

概述

全球边缘人工智慧市场在 2023 年达到 168 亿美元，预计到 2031 年将达到 738 亿美元，2024-2031 年预测期间CAGR为 20.6%。

透过边缘运算技术（例如专用处理器和硬体加速器）的改进，边缘的处理能力得到提高。由于处理能力增强，复杂的人工智慧模型可以在边缘设备上有效地开发和运行，这使得困难的资料分析和即时推理工作更容易完成。

透过将电脑电源移近资料来源，边缘运算可显着降低资料的延迟和传输时间。对于需要即时做出决策的人工智慧应用程序，例如扩增实境、工业自动化和自动驾驶汽车，这种延迟的减少非常重要。边缘运算透过减少延迟来提高系统效能和客户满意度，从而实现更快的人工智慧推理和回应时间。

主要参与者透过推出新产品在全球推广边缘人工智慧的措施日益增多，有助于推动预测期内全球边缘人工智慧市场的成长。例如，2023年7月6日，Silicom与Hailo完成合作，推出Edge AI产品线。 Hailo 的 AI 加速器整合到 Silicom 目前的 Edge 平台中，解决了 Edge AI 应用程式的效能挑战。因此，Silicom 的产品将以极具吸引力的性价比提供边缘视觉处理和人工智慧推理。

北美组织和政府正在对边缘人工智慧基础设施、研发和研究进行战略投资，以在全球市场上保持竞争力。由于商业投资、政府资助和公私合作等倡议，边缘人工智慧产业正在不断发展和创新。根据 5G Americas Omdia 进行的研究，截至 2023 年第三季度，北美地区拥有 1.76 亿个 5G 连接，这意味着上一季度新增了 2,200 万个连接。

动力学

物联网 (IoT) 的日益普及

在网路边界，感测器、摄影机和其他连接设备为物联网设备提供大量资料。边缘人工智慧无需依赖集中式云端伺服器，可以直接在边缘即时处理和分析这些资料，从而实现快速洞察和行动。许多物联网用途（包括连网汽车、智慧家庭和工业自动化）都需要低延迟来实现即时回应。为了满足这一需求，边缘人工智慧在本地分析资料，从而降低延迟并确保快速决策，而不会因将资料发送到远端资料中心而造成延迟。

过去 10 年，物联网的使用量显着增加。 IHS 预计，到 2022 年，使用的物联网设备数量将增加近三倍，从 2015 年的 154.1 亿增加到 426.2 亿。预测表明，这一增长速度将会更快，预计到 2025 年，物联网设备数量将达到 754.4 亿台。推动物联网成长的关键因素是不断扩大的连接选项范围。 5G 网路的可及性和宽频速度的不断提高推动了物联网的发展，这使得设备能够以迄今为止不可思议且高效的速度进行连接。

对自动驾驶汽车和机器人技术的需求不断增长

即时处理来自少数感测器（例如摄影机、光达、雷达和超音波感测器）的大量资讯对于机器人和独立车辆至关重要。透过在组织边缘本地处理讯息，边缘模拟智慧使这些框架能够快速做出选择，并且减少对合併的云端基础的依赖。边缘人工智慧使机器人和自动驾驶汽车能够快速做出重要选择，从而提高安全性和可靠性。该系统可以透过在边缘立即处理资料来对不断变化的环境条件和任何风险做出快速反应，从而降低发生事故的可能性并提高整体性能。

一些主要参与者遵循併购策略，这进一步有助于促进市场成长。例如，2020 年6 月19 日，自动驾驶汽车联盟与边缘运算潮流引领者凌华科技合作，共同利用边缘人造智能，为每个人提供独立驾驶能力。凌华科技的整体愿景是连接个人并积极影响商业和社会。。利用 Autoware 的开源自动驾驶技术，双方将共同打造精明的交通和交通号誌系统。

资料隐私和安全问题

对资料安全和隐私的担忧损害了客户和企业对边缘人工智慧系统收集、分析和保留资料的信心。缺乏信心会阻碍利益相关者共享敏感资料或在边缘部署人工智慧应用程序，从而阻碍边缘人工智慧解决方案的采用和资助。收集、处理和保护个人资料的组织必须遵守严格的限制。遵守这些法规会增加边缘人工智慧实施的成本和复杂性，从而阻碍业务扩张。

随着边缘运算的发展以及世界上连网设备数量的不断增加，边缘人工智慧系统很容易遭受资料外洩、网路攻击和未经授权的存取。其后果会降低消费者对边缘人工智慧技术的信任并阻碍其扩张。有必要在边缘人工智慧设定中整合强大的安全措施，如加密、存取限制、身份验证系统和安全通讯协议，以确保资料隐私和安全。然而，企业发现很难在分散的边缘设定中实施和维护这些安全控制，这阻碍了边缘人工智慧解决方案的采用。

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