Edge Ai Hardware
PUBLISHED: 2024 ID: SMRC26798
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Edge Ai Hardware

Edge AI Hardware Market Forecasts to 2030 - Global Analysis By Processor Type (CPUs (Central Processing Units), GPUs (Graphics Processing Units), DSPs (Digital Signal Processors), NPUs (Neural Processing Units), ASICs (Application-Specific Integrated Circuits), FPGAs (Field-Programmable Gate Arrays) and Other Processor Types), Device Type, Deployment, Application and By Geography

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4.1 (98 reviews)
Published: 2024 ID: SMRC26798

This report covers the impact of COVID-19 on this global market
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According to Stratistics MRC, the Global Edge AI Hardware Market is accounted for $25.61 billion in 2024 and is expected to reach $55.82 billion by 2030 growing at a CAGR of 18.7% during the forecast period. Edge AI hardware refers to specialized computing devices designed to perform artificial intelligence (AI) tasks locally, at or near the data source (the edge) rather than relying on centralized cloud servers. Edge AI hardware enables real-time processing of data from sensors and other sources without requiring constant internet connectivity, making it ideal for applications where speed, privacy, or bandwidth constraints are critical.

According to an article by CNN Business, the South Korean government will invest USD 6.94 billion in artificial intelligence by 2027 as part of efforts to retain a leading global position in cutting-edge semiconductor chips.

Market Dynamics: 

Driver: 

Increasing demand for real-time analytics

Edge AI hardware enables devices to perform complex computations locally, reducing latency and enabling quicker responses to data insights. Industries such as autonomous vehicles, manufacturing, and healthcare require instantaneous analytics for operational efficiency and safety. By deploying Edge AI hardware, organizations can achieve faster insights, improved operational agility, and enhanced responsiveness, thereby meeting the growing demand for real-time analytics in critical applications.

Restraint:

Scalability issues

Scalability issues in Edge AI hardware arise from complexities in deploying and managing distributed systems across diverse environments. Challenges include integrating heterogeneous devices, ensuring seamless interoperability, and managing updates and maintenance remotely. Furthermore, scaling edge AI solutions to accommodate growing data volumes and evolving application requirements requires robust infrastructure and skilled expertise. These factors increase deployment costs and complexity, limiting scalability and hindering widespread adoption. 

Opportunity:

Proliferation of IoT devices

Edge AI hardware is essential for processing this data locally; reducing latency and bandwidth requirements while enhancing real-time decision-making capabilities. This capability is crucial in applications such as smart cities, industrial automation, and healthcare, where rapid data analysis is necessary for operational efficiency and responsiveness. As IoT deployments continue to expand, the demand for efficient, decentralized processing solutions provided by edge AI hardware is expected to rise significantly.

Threat:

Complexity in integration

Complexity in integrating Edge AI hardware arises due to diverse hardware platforms, software frameworks, and compatibility issues with existing IT infrastructures. This complexity hampers market growth by increasing deployment costs, requiring specialized technical expertise, and potentially extending time-to-market for solutions. Lack of standardized protocols and interoperability standards further complicates integration efforts, limiting scalability and interoperability across different edge computing environments.

Covid-19 Impact

The covid-19 pandemic accelerated the adoption of edge AI hardware by highlighting the need for decentralized data processing in remote work setups, healthcare monitoring, and contactless operations. Organizations sought solutions that could ensure real-time data analysis and minimize dependence on centralized infrastructure. This shift drove increased demand for edge AI hardware, particularly in sectors prioritizing safety, efficiency, and continuity during global disruptions.

The servers segment is expected to be the largest during the forecast period

The servers segment is estimated to have a lucrative growth. Edge servers in Edge AI hardware refer to specialized computing devices positioned at the periphery of networks, closer to data sources. They facilitate local processing of AI algorithms, reducing latency and bandwidth consumption by handling data closer to its origin. Edge servers are crucial for applications requiring real-time analytics, such as IoT deployments and autonomous systems, enabling faster decision-making and enhancing overall system efficiency and responsiveness.

The smart cities segment is expected to have the highest CAGR during the forecast period

The smart cities segment is anticipated to witness the highest CAGR growth during the forecast period. Edge AI hardware plays a crucial role in smart cities by enabling real-time data processing and decision-making at the edge of the network. These devices facilitate efficient management of urban infrastructure. By processing data locally, Edge AI hardware reduces latency, improves resource allocation, enhances public safety, and optimizes service delivery, thereby supporting the development and sustainability of smart city initiatives.

Region with largest share:

Asia Pacific is projected to hold the largest market share during the forecast period driven by the proliferation of IoT devices, advancements in 5G infrastructure, and increasing adoption of AI-driven applications across industries such as manufacturing, healthcare, and automotive. Countries like China, Japan, and South Korea are leading in technological innovation and deployment of edge AI solutions. The region's dynamic industrial landscape and government initiatives promoting digital transformation further bolster market expansion.

Region with highest CAGR:

North America is projected to have the highest CAGR over the forecast period driven by the region's technological advancements, particularly in IoT, autonomous systems, and smart manufacturing. Key factors propelling market expansion include increasing investments in smart city initiatives, rising demand for autonomous vehicles, and the proliferation of connected devices in industrial automation and healthcare sectors. North America remains a pivotal region for driving advancements and adoption of Edge AI hardware technologies. 

Key players in the market

Some of the key players profiled in the Edge AI Hardware Market include NVIDIA, Intel, Qualcomm, Google, Synopsys, CEVA Inc., Xilinx, Huawei, Samsung Electronics, NXP Semiconductors, Texas Instruments, Apple and Micron Technology.

Key Developments:

In July 2024, Google launched distributed cloud edge hardware to run AI workloads in or outside its data centers. The Google Distributed Cloud (GDC) air-gapped appliance is mostly for highly regulated organizations that must keep data in-house. The hardware runs the Google Cloud infrastructure stack, data security services and Vertex AI platform. Vertex AI runs models that have been pretrained for various tasks.

In September 2022, NVIDIA introduced the NVIDIA IGX platform for high-precision edge AI, bringing advanced security and proactive safety to sensitive industries such as manufacturing, logistics and healthcare. NVIDIA IGX will help companies build the next generation of software-defined industrial and medical devices that can safely operate in the same environment as humans.

Processor Types Covered:
• CPUs (Central Processing Units)
• GPUs (Graphics Processing Units)
• DSPs (Digital Signal Processors)
• NPUs (Neural Processing Units)
• ASICs (Application-Specific Integrated Circuits)
• FPGAs (Field-Programmable Gate Arrays)
• Other Processor Types

Device Types Covered:
• Servers
• Gateways
• Computing Devices
• Cameras
• Robots
• Drones
• Other Device Types

Deployments Covered:
• On-Premises
• Cloud-Edge
• Fog Computing

Applications Covered:
• Autonomous Vehicles
• Healthcare
• Surveillance & Security
• Consumer Electronics
• Retail
• Smart Cities
• Other Applications

Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan        
o China        
o India        
o Australia  
o New Zealand
o South Korea
o Rest of Asia Pacific    
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa 
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements

Free Customization Offerings: 
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary           
             
2 Preface            
 2.1 Abstract           
 2.2 Stake Holders          
 2.3 Research Scope          
 2.4 Research Methodology         
  2.4.1 Data Mining         
  2.4.2 Data Analysis         
  2.4.3 Data Validation         
  2.4.4 Research Approach         
 2.5 Research Sources          
  2.5.1 Primary Research Sources        
  2.5.2 Secondary Research Sources        
  2.5.3 Assumptions         
             
3 Market Trend Analysis          
 3.1 Introduction          
 3.2 Drivers           
 3.3 Restraints          
 3.4 Opportunities          
 3.5 Threats           
 3.6 Application Analysis         
 3.7 Emerging Markets          
 3.8 Impact of Covid-19          
             
4 Porters Five Force Analysis          
 4.1 Bargaining power of suppliers         
 4.2 Bargaining power of buyers         
 4.3 Threat of substitutes         
 4.4 Threat of new entrants         
 4.5 Competitive rivalry          
             
5 Global Edge AI Hardware Market, By Processor Type       
 5.1 Introduction          
 5.2 CPUs (Central Processing Units)        
 5.3 GPUs (Graphics Processing Units)        
 5.4 DSPs (Digital Signal Processors)        
 5.5 NPUs (Neural Processing Units)        
 5.6 ASICs (Application-Specific Integrated Circuits)       
 5.7 FPGAs (Field-Programmable Gate Arrays)       
 5.8 Other Processor Types         
             
6 Global Edge AI Hardware Market, By Device Type        
 6.1 Introduction          
 6.2 Servers           
 6.3 Gateways          
 6.4 Computing Devices          
 6.5 Cameras           
 6.6 Robots           
 6.7 Drones           
 6.8 Other Device Types          
             
7 Global Edge AI Hardware Market, By Deployment        
 7.1 Introduction          
 7.2 On-Premises          
 7.3 Cloud-Edge          
 7.4 Fog Computing          
             
8 Global Edge AI Hardware Market, By Application        
 8.1 Introduction          
 8.2 Autonomous Vehicles         
 8.3 Healthcare          
 8.4 Surveillance & Security         
 8.5 Consumer Electronics         
 8.6 Retail           
 8.7 Smart Cities          
 8.8 Other Applications          
             
9 Global Edge AI Hardware Market, By Geography        
 9.1 Introduction          
 9.2 North America          
  9.2.1 US          
  9.2.2 Canada          
  9.2.3 Mexico          
 9.3 Europe           
  9.3.1 Germany          
  9.3.2 UK          
  9.3.3 Italy          
  9.3.4 France          
  9.3.5 Spain          
  9.3.6 Rest of Europe         
 9.4 Asia Pacific          
  9.4.1 Japan          
  9.4.2 China          
  9.4.3 India          
  9.4.4 Australia          
  9.4.5 New Zealand         
  9.4.6 South Korea         
  9.4.7 Rest of Asia Pacific         
 9.5 South America          
  9.5.1 Argentina         
  9.5.2 Brazil          
  9.5.3 Chile          
  9.5.4 Rest of South America        
 9.6 Middle East & Africa         
  9.6.1 Saudi Arabia         
  9.6.2 UAE          
  9.6.3 Qatar          
  9.6.4 South Africa         
  9.6.5 Rest of Middle East & Africa        
             
10 Key Developments           
 10.1 Agreements, Partnerships, Collaborations and Joint Ventures      
 10.2 Acquisitions & Mergers         
 10.3 New Product Launch         
 10.4 Expansions          
 10.5 Other Key Strategies         
             
11 Company Profiling           
 11.1 NVIDIA           
 11.2 Intel           
 11.3 Qualcomm          
 11.4 Google           
 11.5 Synopsys           
 11.6 CEVA Inc.           
 11.7 Xilinx           
 11.8 Huawei           
 11.9 Samsung Electronics         
 11.10 NXP Semiconductors         
 11.11 Texas Instruments          
 11.12 Apple           
 11.13 Micron Technology          
             
List of Tables            
1 Global Edge AI Hardware Market Outlook, By Region (2022-2030) ($MN)      
2 Global Edge AI Hardware Market Outlook, By Processor Type (2022-2030) ($MN)     
3 Global Edge AI Hardware Market Outlook, By CPUs (Central Processing Units) (2022-2030) ($MN)   
4 Global Edge AI Hardware Market Outlook, By GPUs (Graphics Processing Units) (2022-2030) ($MN)   
5 Global Edge AI Hardware Market Outlook, By DSPs (Digital Signal Processors) (2022-2030) ($MN)   
6 Global Edge AI Hardware Market Outlook, By NPUs (Neural Processing Units) (2022-2030) ($MN)   
7 Global Edge AI Hardware Market Outlook, By ASICs (Application-Specific Integrated Circuits) (2022-2030) ($MN)  
8 Global Edge AI Hardware Market Outlook, By FPGAs (Field-Programmable Gate Arrays) (2022-2030) ($MN)  
9 Global Edge AI Hardware Market Outlook, By Other Processor Types (2022-2030) ($MN)    
10 Global Edge AI Hardware Market Outlook, By Device Type (2022-2030) ($MN)     
11 Global Edge AI Hardware Market Outlook, By Servers (2022-2030) ($MN)      
12 Global Edge AI Hardware Market Outlook, By Gateways (2022-2030) ($MN)     
13 Global Edge AI Hardware Market Outlook, By Computing Devices (2022-2030) ($MN)    
14 Global Edge AI Hardware Market Outlook, By Cameras (2022-2030) ($MN)     
15 Global Edge AI Hardware Market Outlook, By Robots (2022-2030) ($MN)      
16 Global Edge AI Hardware Market Outlook, By Drones (2022-2030) ($MN)      
17 Global Edge AI Hardware Market Outlook, By Other Device Types (2022-2030) ($MN)    
18 Global Edge AI Hardware Market Outlook, By Deployment (2022-2030) ($MN)     
19 Global Edge AI Hardware Market Outlook, By On-Premises (2022-2030) ($MN)     
20 Global Edge AI Hardware Market Outlook, By Cloud-Edge (2022-2030) ($MN)     
21 Global Edge AI Hardware Market Outlook, By Fog Computing (2022-2030) ($MN)     
22 Global Edge AI Hardware Market Outlook, By Application (2022-2030) ($MN)     
23 Global Edge AI Hardware Market Outlook, By Autonomous Vehicles (2022-2030) ($MN)    
24 Global Edge AI Hardware Market Outlook, By Healthcare (2022-2030) ($MN)     
25 Global Edge AI Hardware Market Outlook, By Surveillance & Security (2022-2030) ($MN)    
26 Global Edge AI Hardware Market Outlook, By Consumer Electronics (2022-2030) ($MN)    
27 Global Edge AI Hardware Market Outlook, By Retail (2022-2030) ($MN)      
28 Global Edge AI Hardware Market Outlook, By Smart Cities (2022-2030) ($MN)     
29 Global Edge AI Hardware Market Outlook, By Other Applications (2022-2030) ($MN)    
             
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.

List of Figures

RESEARCH METHODOLOGY


Research Methodology

We at Stratistics opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.

Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.

Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.

Data Mining

The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.

Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.

Data Analysis

From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:

  • Product Lifecycle Analysis
  • Competitor analysis
  • Risk analysis
  • Porters Analysis
  • PESTEL Analysis
  • SWOT Analysis

The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.


Data Validation

The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.

We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.

The data validation involves the primary research from the industry experts belonging to:

  • Leading Companies
  • Suppliers & Distributors
  • Manufacturers
  • Consumers
  • Industry/Strategic Consultants

Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.


For more details about research methodology, kindly write to us at info@strategymrc.com

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