Edge Artificial Intelligence Market by Processor, Component, Source, End-Use, Application

Description

List of Tables

[194 Pages Report] The Edge Artificial Intelligence Market size was estimated at USD 1.06 billion in 2023 and expected to reach USD 1.31 billion in 2024, at a CAGR 24.71% to reach USD 4.98 billion by 2030.

Edge artificial intelligence (AI) refers to a system where AI algorithms are processed locally on a hardware device. The device undertakes data processing and decision-making in real time without relying on the cloud or centralized data centers. This decentralized approach is achievable through the integration of advanced AI and machine learning capabilities directly into edge devices, such as smartphones, IoT (Internet of Things) devices, and autonomous vehicles. Increased demand for low-latency processing and real-time decision-making capabilities in various industries are driving the development and adoption of edge AI technology. The proliferation of IoT devices and the need to process vast amounts of data at the source without overloading network bandwidth further increases the demand for these innovative solutions, thus expanding the scope of the edge artificial intelligence market. However, concerns over data security and privacy, alongside the complexity of deploying and maintaining AI models on edge devices, present challenges for the market. Despite the challenges, the surge in intelligent applications across healthcare, automotive, and manufacturing sectors presents significant opportunities for edge AI. Advancements in semiconductor technologies and increased investments in AI research can lead to more powerful and efficient edge AI solutions.

KEY MARKET STATISTICS
Base Year [2023]	USD 1.06 billion
Estimated Year [2024]	USD 1.31 billion
Forecast Year [2030]	USD 4.98 billion
CAGR (%)	24.71%

Processor: Increasing preference of ASIC due to its energy efficiency and high performance

An ASIC is designed for a particular use rather than for general-purpose use. In the context of Edge AI, ASICs offer high efficiency and are optimized for specific AI algorithms and models. ASICs are preferred in instances where energy efficiency and high performance for specific tasks are critical. They are ideal for high-volume, embedded devices that require real-time processing, such as IoT devices, autonomous vehicles, and smartphones. The CPU is an important component of a computer that takes out most of the processing tasks. In Edge AI, CPUs can be seen as a more general-purpose processor. CPUs are typically used when flexibility is important. They are competent in performing multiple tasks and are suitable for applications that require complex decision-making capabilities, which do not necessarily need the high-speed processing of ASICs or GPUs. GPUs are designed to render graphics but have become beneficial in accelerating deep learning tasks due to their parallel processing capabilities. GPUs are the go-to processors for machine learning training and inference tasks due to their ability to handle multiple operations simultaneously. They are ideal for video analytics, AI model training, and any application where parallel processing can significantly reduce computation times.

Source: Edge-based biometric systems provide faster response times and reduce bandwidth load on networks

Biometric data involves the collection and analysis of physical and behavioral attributes that enable the unique identification of individuals, including fingerprints, facial recognition, iris scans, and voice patterns. In the context of edge AI, processing biometric data locally reduces latency, enhances privacy, and ensures operation even with intermittent connectivity. Mobile data encompasses the vast amount of information generated by mobile devices, such as location data, app usage statistics, and user behavior insights. Leveraging edge AI for processing mobile data can greatly enhance the personalization of services and real-time decision-making capacity. Sensor data refers to the output from physical sensors embedded in devices or environments, capturing a range of indicators such as temperature, humidity, vibration, and motion. Edge AI enables the immediate processing of this data for efficient operational responses. Speech recognition technology enables devices to understand and process human voice commands and convert them into actionable data. When integrated with edge AI, it facilitates seamless interaction and reduces the dependency on cloud processing. Video and image recognition involves analyzing visual content to identify objects, faces, scenes, and activities. Edge AI accelerates this task by processing content directly on devices, including cameras and smartphones, thus supporting applications such as surveillance, retail analytics, and autonomous driving.

End-Use: Rising adoption of edge AI by government and public sector, emphasizing service delivery and data security

The automotive industry is increasingly integrating edge AI solutions for various applications such as autonomous driving, predictive maintenance, and enhanced user experiences. Edge AI enables cars to respond quickly to changing environments by processing data locally, eliminating delays that could potentially compromise passenger safety. Energy and utilities employ edge AI for managing grid operations, optimizing energy flow, and predictive maintenance of infrastructure. The need for operational efficiency and advanced monitoring of energy distribution networks is paramount, as edge AI helps utilities make real-time decisions based on instantaneous data. In the government and public sector, edge AI is utilized for smart city initiatives, public safety, and transportation systems. The need in this sector is to improve service delivery while ensuring the privacy and security of the citizens. The healthcare sector benefits from edge AI through enhanced patient monitoring, medical imaging analysis, and in-hospital logistics. The need for edge AI stems from the urgency to process large volumes of sensitive health data quickly and locally for timely decision-making. Edge AI in manufacturing is aimed at quality control, predictive maintenance, and supply chain optimization. The need for this technology is particularly acute due to the large quantity of data points generated on the factory floor that require immediate analysis to improve productivity and safety. Telecom operators use edge AI for network optimization, customer experience enhancement, and predictive analytics.

Application: Deployment of edge AI in smart wearables to offer more accurate data analysis and faster processing

Edge AI in access management encompasses biometric authentication, security systems, and smart lock technologies. The need for enhanced security protocols in both private and public sectors drives preference for these solutions. Edge AI allows real-time data processing, thereby reducing latency and improving decision-making speed. Edge AI in autonomous vehicles (AVs) refers to the use of AI algorithms processed locally to perform tasks such as path planning, object detection, and decision-making in real time. The increase in R&D and the push for safer roads give edge AI in AVs a growing preference. Energy management utilizing edge AI involves optimizing energy usage and reducing operational costs in commercial and industrial settings. Its preference stems from the pursuit of sustainable and energy-efficient operations. Precision agriculture with edge AI allows for smart farming techniques, including crop monitoring and soil condition analysis. The rising need for food security and sustainable agricultural practices enhances its preference. Smart wearables using edge AI include fitness trackers and medical monitoring devices that provide real-time insights into personal health metrics. Consumer demand for personalized health data and convenience drives the expansion and preference for these devices. Edge AI in telemetry involves collecting and processing data from remote or inaccessible areas in fields such as aerospace and automotive. Preferences for edge AI telemetry are fueled by the need for real-time data processing in data transmission. Video surveillance with edge AI is used for real-time threat detection and analysis in sectors such as retail and public security. The preference for edge AI in surveillance systems is due to their effectiveness in reducing false alarms and providing immediate analysis.

Regional Insights

The market for edge artificial intelligence (AI) in the Americas is experiencing robust growth, driven by the robust adoption of cloud-based technologies and the increasing prevalence of IoT devices. North America, in particular, is a hub for technological innovation, with well-established players expanding their offerings in edge AI solutions. In the EMEA region, the edge AI market is marked by a dynamic and diverse landscape. Europe's strict privacy regulations, such as the General Data Protection Regulation (GDPR), are catalyzing the shift toward local data processing, thus fueling the growth of edge AI technologies. The Middle East is leveraging AI at the edge for smart city initiatives and to enhance oil and gas operations. Meanwhile, investments in Africa are growing, particularly in areas including agritech and healthcare, where edge AI can greatly improve efficiency and accessibility. The APAC region demonstrates significant potential and is expected to witness the highest growth rate in the edge AI market, owing to the increasing penetration of AI technologies and government support in China, South Korea, and Japan. APAC's large manufacturing base is actively incorporating AI edge computing for real-time process optimization. Furthermore, the region's burgeoning consumer electronics sector provides a fertile ground for embedding edge AI into consumer devices.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Edge Artificial Intelligence Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Edge Artificial Intelligence Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Edge Artificial Intelligence Market, highlighting leading vendors and their innovative profiles. These include Adlink Technology, Inc., Amazon Web Services Inc., Anagog Ltd., BrainChip Holdings Ltd., Cato Networks, Ltd., ClearBlade, Inc., Cloudera, Inc., Edge Intelligence Software, Inc. by Adapdix, Inc., EdgeConneX, EdgeIQ, Eta Compute Inc., Google LLC by Alphabet Inc., Gorilla Technology Inc., Hewlett Packard Enterprise Company, Intel Corporation, International Business Machines Corporation, Johnson Controls International PLC, Lenovo Group Ltd., Microsoft Corporation, Nutanix, Inc., Octonion SA, Saguna Consulting Services LLC, Synaptics Incorporated, Tata Elxsi Limited, TIBCO Software Inc. by Cloud Software Group, Inc., Valores Corporativos Softtek, S.A. de C.V., and Vapor IO.

Market Segmentation & Coverage

This research report categorizes the Edge Artificial Intelligence Market to forecast the revenues and analyze trends in each of the following sub-markets:

Processor
- ASIC
- CPU
- GPU

Component
- Services
  - Support & Maintenance
  - System Integration & Testing
  - Training & Consulting

Solution
- Platform
- Software Tools

Source
- Biometric Data
- Mobile Data
- Sensor Data
- Speech Recognition
- Video & Image Recognition

End-Use
- Automotive
- Energy and Utilities
- Government & Public Sector
- Healthcare
- Manufacturing
- Telecom

Application
- Access Management
- Autonomous Vehicles
- Energy Management
- Precision Agriculture
- Remote Monitoring & Predictive Maintenance
- Smart Wearables
- Telemetry
- Video Surveillance

Region
- Americas
  - Argentina
  - Brazil
  - Canada
  - Mexico
  - United States
    - California
    - Florida
    - Illinois
    - New York
    - Ohio
    - Pennsylvania
    - Texas
- Asia-Pacific
  - Australia
  - China
  - India
  - Indonesia
  - Japan
  - Malaysia
  - Philippines
  - Singapore
  - South Korea
  - Taiwan
  - Thailand
  - Vietnam

Europe, Middle East & Africa
- Denmark
- Egypt
- Finland
- France
- Germany
- Israel
- Italy
- Netherlands
- Nigeria
- Norway
- Poland
- Qatar
- Russia
- Saudi Arabia
- South Africa
- Spain
- Sweden
- Switzerland
- Turkey
- United Arab Emirates
- United Kingdom

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the Edge Artificial Intelligence Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Edge Artificial Intelligence Market?

3. What are the technology trends and regulatory frameworks in the Edge Artificial Intelligence Market?

4. What is the market share of the leading vendors in the Edge Artificial Intelligence Market?

5. Which modes and strategic moves are suitable for entering the Edge Artificial Intelligence Market?

Product Code: MRR-035590447C15

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Intensifying Usage of Social Media and E-Commerce Platforms
  - 5.1.1.2. Amalgamation of Artificial Intelligence into the Edge Ecosystem
  - 5.1.1.3. Increasing Enterprise Workloads on the Clouds
- 5.1.2. Restraints
  - 5.1.2.1. Privacy and Security Issues Related to Edge AI Solutions
  - 5.1.2.2. Optimization of Edge AI Standards
- 5.1.3. Opportunities
  - 5.1.3.1. Emergence of the 5G Network to bring IT and Telecom Together
  - 5.1.3.2. Increasing Number of Artificial Intelligence Applications
  - 5.1.3.3. Increasing Demand for Enhanced Data Privacy and Security
- 5.1.4. Challenges
  - 5.1.4.1. Interoperability Issues Slowing the Adoption of Edge AI Software
5.2. Market Segmentation Analysis
- 5.2.1. Processor: Increasing preference of ASIC due to its energy efficiency and high performance
- 5.2.2. Source: Edge-based biometric systems provide faster response times and reduce bandwidth load on networks
- 5.2.3. End-Use: Rising adoption of edge AI by government and public sector, emphasizing service delivery and data security
- 5.2.4. Application: Deployment of edge AI in smart wearables to offer more accurate data analysis and faster processing
5.3. Market Trend Analysis
5.4. Cumulative Impact of Russia-Ukraine Conflict
5.5. Cumulative Impact of High Inflation
5.6. Porter's Five Forces Analysis
- 5.6.1. Threat of New Entrants
- 5.6.2. Threat of Substitutes
- 5.6.3. Bargaining Power of Customers
- 5.6.4. Bargaining Power of Suppliers
- 5.6.5. Industry Rivalry
5.7. Value Chain & Critical Path Analysis
5.8. Regulatory Framework Analysis
5.9. Client Customization

6. Edge Artificial Intelligence Market, by Processor

6.1. Introduction
6.2. ASIC
6.3. CPU
6.4. GPU

7. Edge Artificial Intelligence Market, by Component

7.1. Introduction
7.2. Services
7.3. Solution

8. Edge Artificial Intelligence Market, by Source

8.1. Introduction
8.2. Biometric Data
8.3. Mobile Data
8.4. Sensor Data
8.5. Speech Recognition
8.6. Video & Image Recognition

9. Edge Artificial Intelligence Market, by End-Use

9.1. Introduction
9.2. Automotive
9.3. Energy and Utilities
9.4. Government & Public Sector
9.5. Healthcare
9.6. Manufacturing
9.7. Telecom

10. Edge Artificial Intelligence Market, by Application

10.1. Introduction
10.2. Access Management
10.3. Autonomous Vehicles
10.4. Energy Management
10.5. Precision Agriculture
10.6. Remote Monitoring & Predictive Maintenance
10.7. Smart Wearables
10.8. Telemetry
10.9. Video Surveillance

11. Americas Edge Artificial Intelligence Market

11.1. Introduction
11.2. Argentina
11.3. Brazil
11.4. Canada
11.5. Mexico
11.6. United States

12. Asia-Pacific Edge Artificial Intelligence Market

12.1. Introduction
12.2. Australia
12.3. China
12.4. India
12.5. Indonesia
12.6. Japan
12.7. Malaysia
12.8. Philippines
12.9. Singapore
12.10. South Korea
12.11. Taiwan
12.12. Thailand
12.13. Vietnam

13. Europe, Middle East & Africa Edge Artificial Intelligence Market

13.1. Introduction
13.2. Denmark
13.3. Egypt
13.4. Finland
13.5. France
13.6. Germany
13.7. Israel
13.8. Italy
13.9. Netherlands
13.10. Nigeria
13.11. Norway
13.12. Poland
13.13. Qatar
13.14. Russia
13.15. Saudi Arabia
13.16. South Africa
13.17. Spain
13.18. Sweden
13.19. Switzerland
13.20. Turkey
13.21. United Arab Emirates
13.22. United Kingdom

14. Competitive Landscape

14.1. Market Share Analysis, 2023
14.2. FPNV Positioning Matrix, 2023
14.3. Competitive Scenario Analysis
- 14.3.1. BrainChip and MYWAI to Deliver Next-Generation Edge AI Solutions
- 14.3.2. Lenovo Launches Next-Generation Edge AI Solutions for Intelligent Transformation
- 14.3.3. Sidus Space Acquires Edge Artificial Intelligence (AI) Company, Exo-Space

15. Competitive Portfolio

15.1. Key Company Profiles
15.2. Key Product Portfolio