The Global Edge AI Accelerator Market size is expected to reach $47.25 billion by 2031, rising at a market growth of 30.3% CAGR during the forecast period.
The rapid adoption of AI-driven technologies in autonomous vehicles, advanced driver assistance systems (ADAS), and in-vehicle infotainment significantly contributed to the segment’s growth. Consequently, the automotive segment witnessed 24% revenue share in the market in 2023. Additionally, the increasing demand for real-time data processing to enhance vehicle safety, performance, and efficiency further boosted the adoption of edge AI accelerators in the automotive industry.
The convergence of 5G and edge AI is also transforming the automotive industry. Connected vehicles rely on AI-driven systems for navigation, object detection, and driver assistance. With 5G networks, vehicles can communicate with each other and infrastructure in real-time, improving road safety and efficiency. AI accelerators at the edge enable rapid data processing within vehicles, reducing dependence on cloud-based servers and ensuring uninterrupted performance even in low-connectivity areas. In addition, industries such as banking, retail, and transportation are adopting AI-driven surveillance systems to improve security measures. Financial institutions leverage AI-based fraud detection at the edge, while retailers use AI-powered cameras for theft prevention and customer behaviour analysis. AI accelerators enhance real-time decision-making in security applications, making them indispensable for businesses looking to mitigate risks and safeguard assets. Thus, the growing demand for AI-enabled security and surveillance solutions is propelling the market's growth.
However, the challenge of managing and updating AI models on edge devices adds to the complexity. Unlike cloud systems that can be updated centrally, updating AI algorithms on edge devices requires efficient model compression and deployment strategies. As AI models become more sophisticated, edge AI accelerators must find innovative ways to overcome computational and storage limitations while maintaining real-time processing capabilities. Thus, edge AI devices' limited computational power and storage constraints hamper the market's growth.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
The rapid adoption of AI-driven technologies in autonomous vehicles, advanced driver assistance systems (ADAS), and in-vehicle infotainment significantly contributed to the segment’s growth. Consequently, the automotive segment witnessed 24% revenue share in the market in 2023. Additionally, the increasing demand for real-time data processing to enhance vehicle safety, performance, and efficiency further boosted the adoption of edge AI accelerators in the automotive industry.
The convergence of 5G and edge AI is also transforming the automotive industry. Connected vehicles rely on AI-driven systems for navigation, object detection, and driver assistance. With 5G networks, vehicles can communicate with each other and infrastructure in real-time, improving road safety and efficiency. AI accelerators at the edge enable rapid data processing within vehicles, reducing dependence on cloud-based servers and ensuring uninterrupted performance even in low-connectivity areas. In addition, industries such as banking, retail, and transportation are adopting AI-driven surveillance systems to improve security measures. Financial institutions leverage AI-based fraud detection at the edge, while retailers use AI-powered cameras for theft prevention and customer behaviour analysis. AI accelerators enhance real-time decision-making in security applications, making them indispensable for businesses looking to mitigate risks and safeguard assets. Thus, the growing demand for AI-enabled security and surveillance solutions is propelling the market's growth.
However, the challenge of managing and updating AI models on edge devices adds to the complexity. Unlike cloud systems that can be updated centrally, updating AI algorithms on edge devices requires efficient model compression and deployment strategies. As AI models become more sophisticated, edge AI accelerators must find innovative ways to overcome computational and storage limitations while maintaining real-time processing capabilities. Thus, edge AI devices' limited computational power and storage constraints hamper the market's growth.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
Driving and Restraining Factors
Drivers
- Increasing Adoption of AI-Powered Edge Devices for Real-Time Processing
- Growing Demand for AI-Enabled Security and Surveillance Solutions
- Expansion of 5G Networks to Enhance Edge AI Processing Capabilities
Restraints
- Limited Computational Power and Storage Constraints in Edge AI Devices
- High Initial Costs Associated with Edge AI Accelerator Development and Deployment
Opportunities
- Rising Adoption of AI-driven IoT Devices in Smart Homes and Consumer Electronics
- Increasing Investments in AI and Edge Computing by Technology Giants
Challenges
- Power Consumption Constraints for Edge AI Accelerators in Battery-Powered Devices
- Latency and Performance Trade-offs in Real-Time Edge AI Processing
Processor Outlook
Based on processor, the market is characterized into central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuits (ASICs), and field-programmable gate array (FPGA). The central processing unit (CPU) segment garnered 33% revenue share in the market in 2023. This segment's dominance is attributed to the widespread use of CPUs in various AI-enabled edge devices, including smartphones, IoT devices, and industrial systems. Additionally, advancements in multi-core processing and AI-optimized CPUs have enhanced computational efficiency, making them a preferred choice for handling AI workloads at the edge. The growing integration of AI acceleration features in modern CPUs further strengthens their adoption in edge computing applications.Device Outlook
On the basis of device, the market is classified into smartphones, IoT devices, robots, and cameras. The IoT devices segment recorded 33% revenue share in the market in 2023. The rapid expansion of smart home devices, industrial IoT applications, and connected healthcare solutions has contributed to the rising adoption of AI accelerators in IoT devices. Moreover, the need for real-time decision-making, enhanced security, and low-latency processing in IoT ecosystems has strengthened the demand for edge AI accelerators within this segment. As industries increasingly focus on automation and AI-driven analytics, the role of edge AI in IoT devices is expected to become even more prominent.
End Use Outlook
By end use, the market is divided into healthcare, automotive, retail, manufacturing, security and surveillance, and others. The manufacturing segment held 18% revenue share in the market in 2023. Adopting AI-driven automation, predictive maintenance, and quality control solutions has fuelled the demand for edge AI accelerators in manufacturing. Moreover, the increasing implementation of smart factories and Industry 4.0 initiatives and the need for real-time operational insights have played a significant role in the segment’s growth.Regional Outlook
Region-wise, the edge AI accelerator market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America segment recorded 38% revenue share in the market in 2023. The region’s dominance can be attributed to the strong presence of technology giants, high investments in AI infrastructure, and widespread adoption of AI-driven applications across healthcare, automotive, and finance industries. Additionally, government initiatives supporting AI research and development further contributed to North America’s market expansion. The growing demand for real-time data processing and intelligent edge devices has also fuelled market growth in the region.List of Key Companies Profiled
- Apple, Inc.
- Google LLC (Alphabet Inc.)
- Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- IBM Corporation
- Intel Corporation
- NVIDIA Corporation
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- AMBARELLA, INC.
- Microsoft Corporation
Market Report Segmentation
By Processor
- Central Processing Unit (CPU)
- Graphics Processing Unit (GPU)
- Application-Specific Integrated Circuits (ASICs)
- Other Processor
By Device
- Smartphones
- IoT Devices
- Robots
- Cameras
By End Use
- Automotive
- Healthcare
- Manufacturing
- Security & Surveillance
- Retail
- Other End Use
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. Global Edge AI Accelerator Market by Processor
Chapter 6. Global Edge AI Accelerator Market by Device
Chapter 7. Global Edge AI Accelerator Market by End Use
Chapter 8. Global Edge AI Accelerator Market by Region
Chapter 9. Company Profiles
Companies Mentioned
- Apple, Inc.
- Google LLC (Alphabet Inc.)
- Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- IBM Corporation
- Intel Corporation
- NVIDIA Corporation
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- AMBARELLA, INC.
- Microsoft Corporation
Methodology
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