This report presents the worldwide Deep Learning Chipset market size (value, production and consumption), splits the breakdown (data status 2013-2018 and forecast to 2025), by manufacturers, region, type and application.
This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter’s Five Forces Analysis.
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Deep learning technology is driving the evolution of artificial intelligence (AI) and has become one of the hottest topics of discussion within the technology world and beyond. Given the rate at which deep learning is progressing, some industry observers are predicting it will bring about a doomsday scenario, while others strive for a time when the technology can transform business processes and create new business models through scalable, more efficient automation and predictive capabilities. The current market climate is ripe for innovation in hardware in general, and chipsets more specifically.
United States has the largest global export quantity and manufacturers in Deep Learning Chipset market, while the EU is the second sales volume market for Deep Learning Chipset in 2016.
In the industry, NVIDIA profits most in 2016 and recent years, while Intel and IBM ranked 2 and 3.The market share of them is 26.72%, 20.57% and 12.56% in 2016.The gap of market share is keep on enlarged due to different strategy.
Deep Learning Chipset technology is not mature now, and new enterprises can not surpass existing famous brands on reputation or design in the short term. So, the study group recommends the new entrants need to be considered carefully before enter into this field.
The Deep Learning Chipset market was valued at 870 Million US$ in 2017 and is projected to reach 10900 Million US$ by 2025, at a CAGR of 37.1% during the forecast period. In this study, 2017 has been considered as the base year and 2018 to 2025 as the forecast period to estimate the market size for Deep Learning Chipset.
The following manufacturers are covered in this report:
Deep Learning Chipset Breakdown Data by Type
Graphics Processing Units (GPUs)
Central Processing Units (CPUs)
Application Specific Integrated Circuits (ASICs)
Field Programmable Gate Arrays (FPGAs)
Deep Learning Chipset Breakdown Data by Application
Aerospace, Military & Defense
Deep Learning Chipset Production by Region
Deep Learning Chipset Consumption by Region
Rest of Europe
Central & South America
Rest of South America
Middle East & Africa
Rest of Middle East & Africa
The study objectives are:
To analyze and research the global Deep Learning Chipset status and future forecastinvolving, production, revenue, consumption, historical and forecast.
To present the key Deep Learning Chipset manufacturers, production, revenue, market share, and recent development.
To split the breakdown data by regions, type, manufacturers and applications.
To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends, drivers, influence factors in global and regions.
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
In this study, the years considered to estimate the market size of Deep Learning Chipset :
History Year: 2013 – 2017
Base Year: 2017
Estimated Year: 2018
Forecast Year: 2018 – 2025
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This report includes the estimation of market size for value (million USD) and volume (K Units). Both top-down and bottom-up approaches have been used to estimate and validate the market size of Deep Learning Chipset market, to estimate the size of various other dependent submarkets in the overall market. Key players in the market have been identified through secondary research, and their market shares have been determined through primary and secondary research. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified primary sources.
For the data information by region, company, type and application, 2017 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.