Global Deep Learning Chipsets Industry Research Report 2021 Segmented by Major Market Players, Types, Applications and Countries Forecast to 2027
The global Deep Learning Chipsets market is expected to reach US$ XX Million by 2027, with a CAGR of XX% from 2020 to 2027, based on newly published report.
The prime objective of this report is to provide the insights on the post COVID-19 impact which will help market players in this field evaluate their business approaches. Also, this report covers market segmentation by major market verdors, types, applications/end users and geography(North America, East Asia, Europe, South Asia, Southeast Asia, Middle East, Africa, Oceania, South America).
By Market Verdors:
Google
Intel
Xilinx
AMD
NVIDIA
ARM
Qualcomm
IBM
Graphcore
BrainChip
Mobileye
Wave Computing
CEVA
Movidius
Nervana Systems
Amazon
Cerebras Systems
Facebook
By Types:
CPUs
GPUs
FPGAs
ASICs
SoC Accelerators
Others
By Applications:
Automotive
Smart Cameras
Robots
Drones
Mobile Phones
Others
Key Indicators Analysed
Market Players & Competitor Analysis: The report covers the key players of the industry including Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price and Gross Margin 2016-2027 & Sales with a thorough analysis of the market’s competitive landscape and detailed information on vendors and comprehensive details of factors that will challenge the growth of major market vendors.
Global and Regional Market Analysis: The report includes Global & Regional market status and outlook 2016-2027. Further the report provides break down details about each region & countries covered in the report. Identifying its sales, sales volume & revenue forecast. With detailed analysis by types and applications.
Market Trends: Market key trends which include Increased Competition and Continuous Innovations.
Opportunities and Drivers: Identifying the Growing Demands and New Technology
Porters Five Force Analysis: The report provides with the state of competition in industry depending on five basic forces: threat of new entrants, bargaining power of suppliers, bargaining power of buyers, threat of substitute products or services, and existing industry rivalry.
Key Reasons to Purchase
To gain insightful analyses of the market and have comprehensive understanding of the global market and its commercial landscape.
Assess the production processes, major issues, and solutions to mitigate the development risk.
To understand the most affecting driving and restraining forces in the market and its impact in the global market.
Learn about the market strategies that are being adopted by leading respective organizations.
To understand the future outlook and prospects for the market.
Besides the standard structure reports, we also provide custom research according to specific requirements.
1 Report Overview
1.1 Study Scope and Definition
1.2 Research Methodology
1.2.1 Methodology/Research Approach
1.2.2 Data Source
1.3 Key Market Segments
1.4 Players Covered: Ranking by Deep Learning Chipsets Revenue
1.5 Market Analysis by Type
1.5.1 Global Deep Learning Chipsets Market Size Growth Rate by Type: 2021 VS 2027
1.5.2 CPUs
1.5.3 GPUs
1.5.4 FPGAs
1.5.5 ASICs
1.5.6 SoC Accelerators
1.5.7 Others
1.6 Market by Application
1.6.1 Global Deep Learning Chipsets Market Share by Application: 2022-2027
1.6.2 Automotive
1.6.3 Smart Cameras
1.6.4 Robots
1.6.5 Drones
1.6.6 Mobile Phones
1.6.7 Others
1.7 Coronavirus Disease 2019 (Covid-19) Impact Will Have a Severe Impact on Global Growth
1.7.1 Covid-19 Impact: Global GDP Growth, 2019, 2021 and 2022 Projections
1.7.2 Covid-19 Impact: Commodity Prices Indices
1.7.3 Covid-19 Impact: Global Major Government Policy
1.8 Study Objectives
1.9 Years Considered
2 Global Deep Learning Chipsets Market Trends and Growth Strategy
2.1 Market Top Trends
2.2 Market Drivers
2.3 Market Challenges
2.4 Porter’s Five Forces Analysis
2.5 Market Growth Strategy
2.6 SWOT Analysis
3 Global Deep Learning Chipsets Market Players Profiles
3.1 Google
3.1.1 Google Company Profile
3.1.2 Google Deep Learning Chipsets Product Specification
3.1.3 Google Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.2 Intel
3.2.1 Intel Company Profile
3.2.2 Intel Deep Learning Chipsets Product Specification
3.2.3 Intel Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.3 Xilinx
3.3.1 Xilinx Company Profile
3.3.2 Xilinx Deep Learning Chipsets Product Specification
3.3.3 Xilinx Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.4 AMD
3.4.1 AMD Company Profile
3.4.2 AMD Deep Learning Chipsets Product Specification
3.4.3 AMD Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.5 NVIDIA
3.5.1 NVIDIA Company Profile
3.5.2 NVIDIA Deep Learning Chipsets Product Specification
3.5.3 NVIDIA Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.6 ARM
3.6.1 ARM Company Profile
3.6.2 ARM Deep Learning Chipsets Product Specification
3.6.3 ARM Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.7 Qualcomm
3.7.1 Qualcomm Company Profile
3.7.2 Qualcomm Deep Learning Chipsets Product Specification
3.7.3 Qualcomm Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.8 IBM
3.8.1 IBM Company Profile
3.8.2 IBM Deep Learning Chipsets Product Specification
3.8.3 IBM Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.9 Graphcore
3.9.1 Graphcore Company Profile
3.9.2 Graphcore Deep Learning Chipsets Product Specification
3.9.3 Graphcore Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.10 BrainChip
3.10.1 BrainChip Company Profile
3.10.2 BrainChip Deep Learning Chipsets Product Specification
3.10.3 BrainChip Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.11 Mobileye
3.11.1 Mobileye Company Profile
3.11.2 Mobileye Deep Learning Chipsets Product Specification
3.11.3 Mobileye Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.12 Wave Computing
3.12.1 Wave Computing Company Profile
3.12.2 Wave Computing Deep Learning Chipsets Product Specification
3.12.3 Wave Computing Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.13 CEVA
3.13.1 CEVA Company Profile
3.13.2 CEVA Deep Learning Chipsets Product Specification
3.13.3 CEVA Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.14 Movidius
3.14.1 Movidius Company Profile
3.14.2 Movidius Deep Learning Chipsets Product Specification
3.14.3 Movidius Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.15 Nervana Systems
3.15.1 Nervana Systems Company Profile
3.15.2 Nervana Systems Deep Learning Chipsets Product Specification
3.15.3 Nervana Systems Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.16 Amazon
3.16.1 Amazon Company Profile
3.16.2 Amazon Deep Learning Chipsets Product Specification
3.16.3 Amazon Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.17 Cerebras Systems
3.17.1 Cerebras Systems Company Profile
3.17.2 Cerebras Systems Deep Learning Chipsets Product Specification
3.17.3 Cerebras Systems Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
3.18 Facebook
3.18.1 Facebook Company Profile
3.18.2 Facebook Deep Learning Chipsets Product Specification
3.18.3 Facebook Deep Learning Chipsets Production Capacity, Revenue, Price and Gross Margin (2016-2021)
4 Global Deep Learning Chipsets Market Competition by Market Players
4.1 Global Deep Learning Chipsets Production Capacity Market Share by Market Players (2016-2021)
4.2 Global Deep Learning Chipsets Revenue Market Share by Market Players (2016-2021)
4.3 Global Deep Learning Chipsets Average Price by Market Players (2016-2021)
5 Global Deep Learning Chipsets Production by Regions (2016-2021)
5.1 North America
5.1.1 North America Deep Learning Chipsets Market Size (2016-2021)
5.1.2 Deep Learning Chipsets Key Players in North America (2016-2021)
5.1.3 North America Deep Learning Chipsets Market Size by Type (2016-2021)
5.1.4 North America Deep Learning Chipsets Market Size by Application (2016-2021)
5.2 East Asia
5.2.1 East Asia Deep Learning Chipsets Market Size (2016-2021)
5.2.2 Deep Learning Chipsets Key Players in East Asia (2016-2021)
5.2.3 East Asia Deep Learning Chipsets Market Size by Type (2016-2021)
5.2.4 East Asia Deep Learning Chipsets Market Size by Application (2016-2021)
5.3 Europe
5.3.1 Europe Deep Learning Chipsets Market Size (2016-2021)
5.3.2 Deep Learning Chipsets Key Players in Europe (2016-2021)
5.3.3 Europe Deep Learning Chipsets Market Size by Type (2016-2021)
5.3.4 Europe Deep Learning Chipsets Market Size by Application (2016-2021)
5.4 South Asia
5.4.1 South Asia Deep Learning Chipsets Market Size (2016-2021)
5.4.2 Deep Learning Chipsets Key Players in South Asia (2016-2021)
5.4.3 South Asia Deep Learning Chipsets Market Size by Type (2016-2021)
5.4.4 South Asia Deep Learning Chipsets Market Size by Application (2016-2021)
5.5 Southeast Asia
5.5.1 Southeast Asia Deep Learning Chipsets Market Size (2016-2021)
5.5.2 Deep Learning Chipsets Key Players in Southeast Asia (2016-2021)
5.5.3 Southeast Asia Deep Learning Chipsets Market Size by Type (2016-2021)
5.5.4 Southeast Asia Deep Learning Chipsets Market Size by Application (2016-2021)
5.6 Middle East
5.6.1 Middle East Deep Learning Chipsets Market Size (2016-2021)
5.6.2 Deep Learning Chipsets Key Players in Middle East (2016-2021)
5.6.3 Middle East Deep Learning Chipsets Market Size by Type (2016-2021)
5.6.4 Middle East Deep Learning Chipsets Market Size by Application (2016-2021)
5.7 Africa
5.7.1 Africa Deep Learning Chipsets Market Size (2016-2021)
5.7.2 Deep Learning Chipsets Key Players in Africa (2016-2021)
5.7.3 Africa Deep Learning Chipsets Market Size by Type (2016-2021)
5.7.4 Africa Deep Learning Chipsets Market Size by Application (2016-2021)
5.8 Oceania
5.8.1 Oceania Deep Learning Chipsets Market Size (2016-2021)
5.8.2 Deep Learning Chipsets Key Players in Oceania (2016-2021)
5.8.3 Oceania Deep Learning Chipsets Market Size by Type (2016-2021)
5.8.4 Oceania Deep Learning Chipsets Market Size by Application (2016-2021)
5.9 South America
5.9.1 South America Deep Learning Chipsets Market Size (2016-2021)
5.9.2 Deep Learning Chipsets Key Players in South America (2016-2021)
5.9.3 South America Deep Learning Chipsets Market Size by Type (2016-2021)
5.9.4 South America Deep Learning Chipsets Market Size by Application (2016-2021)
5.10 Rest of the World
5.10.1 Rest of the World Deep Learning Chipsets Market Size (2016-2021)
5.10.2 Deep Learning Chipsets Key Players in Rest of the World (2016-2021)
5.10.3 Rest of the World Deep Learning Chipsets Market Size by Type (2016-2021)
5.10.4 Rest of the World Deep Learning Chipsets Market Size by Application (2016-2021)
6 Global Deep Learning Chipsets Consumption by Region (2016-2021)
6.1 North America
6.1.1 North America Deep Learning Chipsets Consumption by Countries
6.1.2 United States
6.1.3 Canada
6.1.4 Mexico
6.2 East Asia
6.2.1 East Asia Deep Learning Chipsets Consumption by Countries
6.2.2 China
6.2.3 Japan
6.2.4 South Korea
6.3 Europe
6.3.1 Europe Deep Learning Chipsets Consumption by Countries
6.3.2 Germany
6.3.3 United Kingdom
6.3.4 France
6.3.5 Italy
6.3.6 Russia
6.3.7 Spain
6.3.8 Netherlands
6.3.9 Switzerland
6.3.10 Poland
6.4 South Asia
6.4.1 South Asia Deep Learning Chipsets Consumption by Countries
6.4.2 India
6.5 Southeast Asia
6.5.1 Southeast Asia Deep Learning Chipsets Consumption by Countries
6.5.2 Indonesia
6.5.3 Thailand
6.5.4 Singapore
6.5.5 Malaysia
6.5.6 Philippines
6.6 Middle East
6.6.1 Middle East Deep Learning Chipsets Consumption by Countries
6.6.2 Turkey
6.6.3 Saudi Arabia
6.6.4 Iran
6.6.5 United Arab Emirates
6.7 Africa
6.7.1 Africa Deep Learning Chipsets Consumption by Countries
6.7.2 Nigeria
6.7.3 South Africa
6.8 Oceania
6.8.1 Oceania Deep Learning Chipsets Consumption by Countries
6.8.2 Australia
6.9 South America
6.9.1 South America Deep Learning Chipsets Consumption by Countries
6.9.2 Brazil
6.9.3 Argentina
6.10 Rest of the World
6.10.1 Rest of the World Deep Learning Chipsets Consumption by Countries
7 Global Deep Learning Chipsets Production Forecast by Regions (2022-2027)
7.1 Global Forecasted Production of Deep Learning Chipsets (2022-2027)
7.2 Global Forecasted Revenue of Deep Learning Chipsets (2022-2027)
7.3 Global Forecasted Price of Deep Learning Chipsets (2022-2027)
7.4 Global Forecasted Production of Deep Learning Chipsets by Region (2022-2027)
7.4.1 North America Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.2 East Asia Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.3 Europe Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.4 South Asia Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.5 Southeast Asia Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.6 Middle East Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.7 Africa Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.8 Oceania Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.9 South America Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.4.10 Rest of the World Deep Learning Chipsets Production, Revenue Forecast (2022-2027)
7.5 Forecast by Type and by Application (2022-2027)
7.5.1 Global Sales Volume, Sales Revenue and Sales Price Forecast by Type (2022-2027)
7.5.2 Global Forecasted Consumption of Deep Learning Chipsets by Application (2022-2027)
8 Global Deep Learning Chipsets Consumption Forecast by Regions (2022-2027)
8.1 North America Forecasted Consumption of Deep Learning Chipsets by Country
8.2 East Asia Market Forecasted Consumption of Deep Learning Chipsets by Country
8.3 Europe Market Forecasted Consumption of Deep Learning Chipsets by Countriy
8.4 South Asia Forecasted Consumption of Deep Learning Chipsets by Country
8.5 Southeast Asia Forecasted Consumption of Deep Learning Chipsets by Country
8.6 Middle East Forecasted Consumption of Deep Learning Chipsets by Country
8.7 Africa Forecasted Consumption of Deep Learning Chipsets by Country
8.8 Oceania Forecasted Consumption of Deep Learning Chipsets by Country
8.9 South America Forecasted Consumption of Deep Learning Chipsets by Country
8.10 Rest of the world Forecasted Consumption of Deep Learning Chipsets by Country
9 Global Deep Learning Chipsets Sales by Type (2016-2027)
9.1 Global Deep Learning Chipsets Historic Market Size by Type (2016-2021)
9.2 Global Deep Learning Chipsets Forecasted Market Size by Type (2022-2027)
10 Global Deep Learning Chipsets Consumption by Application (2016-2027)
10.1 Global Deep Learning Chipsets Historic Market Size by Application (2016-2021)
10.2 Global Deep Learning Chipsets Forecasted Market Size by Application (2022-2027)
11 Global Deep Learning Chipsets Manufacturing Cost Analysis
11.1 Deep Learning Chipsets Key Raw Materials Analysis
11.1.1 Key Raw Materials
11.2 Proportion of Manufacturing Cost Structure
11.3 Manufacturing Process Analysis of Deep Learning Chipsets
12 Global Deep Learning Chipsets Marketing Channel, Distributors, Customers and Supply Chain
12.1 Marketing Channel
12.2 Deep Learning Chipsets Distributors List
12.3 Deep Learning Chipsets Customers
12.4 Deep Learning Chipsets Supply Chain Analysis
13 Analyst's Viewpoints/Conclusions
14 Disclaimer
Methodology/Research Approach
This research study involved the extensive usage of both primary and secondary data sources. The research process involved the study of various factors affecting the industry, including the government policy, market environment, competitive landscape, historical data, present trends in the market, technological innovation, upcoming technologies and the technical progress in related industry, and market risks, opportunities, market barriers and challenges. The following illustrative figure shows the market research methodology applied in this report.Research Programs/Design
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Market Size Estimation
Top-down and bottom-up approaches are used to validate the global Voluntary Carbon Offset market size market and estimate the market size for Company, regions segments, product segments and Application (end users).
The market estimations in this report are based on the marketed sale price of Voluntary Carbon Offset (excluding any discounts provided by the player, distributor, wholesaler or traders). The percentage splits, market share, and breakdowns of the product segments are derived on the basis of weights assigned to each of the segments on the basis of their utilization rate and average sale price. The regional splits of the overall Voluntary Carbon Offset market and its sub-segments are based on the percentage adoption or utilization of the given product in the respective region or country.
Major Company in the market is identified through secondary research and their market revenues determined through primary and secondary research. Secondary research included the research of the annual and financial reports of the top Company; whereas, primary research included extensive interviews of key opinion leaders and industry experts such as experienced front-line staff, directors, CEOs and marketing executives. The percentage splits, market share, Growth Rate and breakdowns of the product markets are determined through using secondary sources and verified through the primary sources.
All possible factors that influence the markets included in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data. The market size for top-level markets and sub-segments is normalized, and the effect of inflation, economic downturns, and regulatory & policy changes or other factors are not accounted for in the market forecast. This data is combined and added with detailed inputs and analysis from Market Intellix and presented in this report
The following figure shows an illustrative representation of the overall market size estimation process used for this study.
Market Breakdown and Data Triangulation
After complete market engineering with calculations for market statistics; market size estimations; market forecasting; market breakdown; and data triangulation, extensive primary research was conducted to gather information and verify and validate the critical numbers arrived at. In the complete market engineering process, both top-down and bottom-up approaches were extensively used, along with several data triangulation methods, to perform market estimation and market forecasting for the overall market segments and sub-segments listed in this report. Extensive qualitative and further quantitative analysis is also done from all the numbers arrived at in the complete market engineering process to list key information throughout the report.
Data Source
Secondary Sources
Secondary sources include such as press releases, annual reports, non-profit organizations, industry associations, governmental agencies and customs data, etc. This research study involves the usage of widespread secondary sources, directories, databases such as Bloomberg Business, Wind Info, Hoovers, Factiva (Dow Jones & Company), and TRADING ECONOMICS, and News Network, statista, Federal Reserve Economic Data, annual reports, BIS Statistics, ICIS; company house documents; CAS(American Chemical Society); investor presentations; and SEC filings of companies. Secondary research was used to identify and collect information useful for the extensive, technical, market-oriented, and Hospitals study of the Voluntary Carbon Offset market. It was also used to obtain important information about the top companies, market classification and segmentation according to industry trends to the bottom-most level, and key developments related to market and technology perspectives.
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Primary Sources
In the primary research process, various sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. The primary sources from the supply side include product Company (and their competitors), opinion leaders, industry experts, research institutions, distributors, dealer and traders, as well as the raw materials suppliers and producers, etc.
The primary sources from the demand side include industry experts such as business leaders, marketing and sales directors, technology and innovation directors, supply chain executive, end use (product buyers), and related key executives from various key companies and organizations operating in the global market.
Primary research was conducted to identify segmentation Type, product price range, product Application, key Company, raw materials supply and the downstream demand, industry status and outlook, and key market dynamics such as risks, influence factors, opportunities, market barriers, industry trends, and key player strategies.
Key Executives Interviewed
Key Data Information from Primary Sources
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