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Global Edge AI Hardware Market to reach USD XXXmillion by 2027.

Global Edge AI Hardware Market Size study, by Device (Smartphones , Surveillance, cameras , Robots, Wearables , Edge servers , Smart speakers , Automotive, Smart mirrors), by Power Consumption (Less than 1 W, 1-3 W, 3-5 W,5-19 W, More than 10 W), by Processor (CPU, GPU, ASIC, Others), and Regional Forecasts 2021-2027

Product Code: ICTNGT-50412741
Publish Date: 10-09-2021
Page: 200

Global Edge AI Hardware Market is valued approximately at USD XXX in 2020 and is anticipated to grow with a healthy growth rate of more than 17.9% over the forecast period 2021-2027. Edge AI hardware refers to devices and equipment that are majorly used for processing and power artificial intelligent-based robots and devices used through Internet of things. The global Edge AI Hardware market is being driven by the rising demand for low latency and the real time processing data on edge devices , emergence of AL coprocessors for the edge computing as well as rapid growth in the number of intelligent applications. Furthermore, the dedicated AI processors for on-device image analytics and growth in demand for edge computing in IoT will provide new opportunities for the global Edge AI Hardware industry. For instance, according to Statista, in year 2018, there were 22 billion internet of things (IoT) connected devices in use across the world and it is also forecasted that by year 2030 around 50 billion of these IoT connected devices will be in use worldwide, which will create a massive web of interconnected devices and artificial intelligence spanning everything from smartphones to kitchen appliances. As a result, increased number of IOT connected devices will serve as a catalyst for the Edge AI Hardware industry in the future. However, limited on-device training and limited number of AI experts may impede market growth over the forecast period of 2021-2027.

Asia Pacific, North America, Europe, Latin America, and Rest of the World are the key regions considered for the regional analysis of global Edge AI Hardware Market. Increase adoption of AI processor-enabled smartphones owing to growing penetration of smartphones makes Asia Pacific the leading region across the world in terms of market share. Whereas Asia pacific is also anticipated to exhibit the highest growth rate over the forecast period 2021-2027, due to increasing demand for integration with vision processing units to accelerate AI tasks in the region.
Major market player included in this report are:

Intel
Nvidia
Qualcomm technologies
Huawei technologies co, LTD
Samsung Electronics
IBM
Micron Technology
Xilinx
AMD
Google

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Device:
Smartphones
Surveillance cameras
Robots
Wearables
Edge servers
Smart speakers
Automotive
Smart mirrors
By Power Consumption:
Less than 1 W
1-3 W
3-5 W
5-19 W
More than 10 W
By Processor:
CPU
GPU
ASIC
Others

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027.

Target Audience of the Global Edge AI Hardware Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Edge AI Hardware Market , by Region, 2019-2027 (USD Billion)
1.2.2. Edge AI Hardware Market , by Device, 2019-2027 (USD Billion)
1.2.3. Edge AI Hardware Market , by Power Consumption, 2019-2027 (USD Billion)
1.2.4. Edge AI Hardware Market , by Processor , 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Edge AI Hardware Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Edge AI Hardware Market Dynamics
3.1. Edge AI Hardware Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Growth in demand for low latency and real-time processing on edge devices
3.1.1.2. Emergence of AI coprocessors for edge computing
3.1.1.3. Rapid growth in the number of intelligent applications
3.1.2. Market Restraint
3.1.2.1. Limited on-device training
3.1.2.2. Limited number of AI experts
3.1.3. Market Opportunities
3.1.3.1. Dedicated AI processors for on-device image analytics
3.1.3.2. Growth in demand for edge computing in IoT
Chapter 4. Global Edge AI Hardware Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
Chapter 5. Global Edge AI Hardware Market , by Device
5.1. Market Snapshot
5.2. Global Edge AI Hardware Market by Device, Performance – Potential Analysis
5.3. Global Edge AI Hardware Market Estimates & Forecasts by Device 2018-2027 (USD Billion)
5.4. Edge AI Hardware Market , Sub Segment Analysis
5.4.1. Smartphones
5.4.2. Surveillance cameras
5.4.3. Robots
5.4.4. Wearables
5.4.5. Edge servers
5.4.6. Smart speakers
5.4.7. Automotive
5.4.8. Smart mirrors
Chapter 6. Global Edge AI Hardware Market , by Power consumption
a. Market Snapshot
6.1. Global Edge AI Hardware Market by Power consumption, Performance – Potential Analysis
6.2. Global Edge AI Hardware Market Estimates & Forecasts by Power Consumption2018-2027 (USD Billion)
6.3. Edge AI Hardware Market , Sub Segment Analysis
6.3.1. Less than 1 W
6.3.2. 1-3 W
6.3.3. 3-5 W
6.3.4. 5-19 W
6.3.5. More than 10 W
Chapter 7. Global Edge AI Hardware Market , by Processor
b. Market Snapshot
7.1. Global Edge AI Hardware Market by Processor , Performance – Potential Analysis
7.2. Global Edge AI Hardware Market Estimates & Forecasts by Processor 2018-2027 (USD Billion)
7.3. Edge AI Hardware Market , Sub Segment Analysis
7.3.1. CPU
7.3.2. GPU
7.3.3. ASIC
7.3.4. Others
Chapter 8. Global Edge AI Hardware Market , Regional Analysis
8.1. Edge AI Hardware Market , Regional Market Snapshot
8.2. North America Edge AI Hardware Market
8.2.1. U.S. Edge AI Hardware Market
8.2.1.1. Device breakdown estimates & forecasts, 2018-2027
8.2.1.2. Power Consumption breakdown estimates & forecasts, 2018-2027
8.2.1.3. Processor breakdown estimates & forecasts, 2018-2027
8.2.2. Canada Edge AI Hardware Market
8.3. Europe Edge AI Hardware Market Snapshot
8.3.1. U.K. Edge AI Hardware Market
8.3.2. Germany Edge AI Hardware Market
8.3.3. France Edge AI Hardware Market
8.3.4. Spain Edge AI Hardware Market
8.3.5. Italy Edge AI Hardware Market
8.3.6. Rest of Europe Edge AI Hardware Market
8.4. Asia-Pacific Edge AI Hardware Market Snapshot
8.4.1. China Edge AI Hardware Market
8.4.2. India Edge AI Hardware Market
8.4.3. Japan Edge AI Hardware Market
8.4.4. Australia Edge AI Hardware Market
8.4.5. South Korea Edge AI Hardware Market
8.4.6. Rest of Asia Pacific Edge AI Hardware Market
8.5. Latin America Edge AI Hardware Market Snapshot
8.5.1. Brazil Edge AI Hardware Market
8.5.2. Mexico Edge AI Hardware Market
8.6. Rest of The World Edge AI Hardware Market
Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. Intel
9.2.1.1. Key Information
9.2.1.2. Overview
9.2.1.3. Financial (Subject to Data Availability)
9.2.1.4. Product Summary
9.2.1.5. Recent Developments
9.2.2. Nvidia
9.2.3. Qualcomm technologies
9.2.4. Huawei technologies co, LTD
9.2.5. Samsung Electronics
9.2.6. IBM
9.2.7. Micron Technology
9.2.8. Xilinx
9.2.9. AMD
9.2.10. Google

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

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Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
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To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
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To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
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Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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