Infinium Global Research has added a new report on Deep
Learning Chipset Market. The report presents analysis of global as well
as regional markets of deep learning chipset market over the period of 2016 to
2024.
Machine learning chip is a multi-processor chip, that has the
ability to learn, reason, and self-correct its algorithm without being clearly
programed .The deep learning chipsets includes CPUs, GPUs, FPGAs, ASICs, SoC
Accelerators, and other chipsets. They are used in the wide range of
applications such as robotics, healthcare, automotive, consumer electronics,
and others owing to its enhanced efficiency and durability.
Increased adoption of cloud based technology and deep learning
usage in big data analytics are the factors driving the growth of the deep
learning chipset market. In addition, Invention of a related computer chip,
which is known as GPU. A single-chip processor creates lighting effects and
transforms objects every time a 3D scene is redrawn or graphic processing unit
is turning out to be very meaningful and effective when it is being applied to
types of calculations required for neural nets. This in turn is fuelling the
growth of the deep learning chipset market. However, low awareness of chipset
besides CPU is likely to hamper the growth of the deep learning chipset market
during the forecast period. Furthermore, Popularity of Internet of Things (IoT)
and increase in demand for automated devices are anticipated to provide
lucrative growth opportunities for the key players in the deep learning chipset
market. On the other hand, Organizations are utilizing deep learning neural
networks to remove valuable perceptions from huge amounts of data for providing
innovative products and improving customer experience thereby, increasing
revenue opportunities.
Among the geographies, North America dominated the deep
learning chipset owing to the increase in demand for semiconductor devices and
automated devices market followed by Asia Pacific. However, the emerging
economies of Japan, China, and India are expected to turn Asia Pacific into the
leading region in terms of revenue. Moreover, the market for deep learning in
Asia Pacific regions is not only growing for the electronics products such as
smartphones, tablets, and PCs, but also medical and automotive products. China and
India are anticipated to drive the global deep learning market owing to the
High economic growth witnessed. Moreover, the growth matrix presented by Infinium
Global Research identifies the key investment markets in the near future, this
will not only help the companies to understand the competitive position in the
global deep learning chipset market but also make strategic decisions to
capture the most lucrative markets in the near future.
The market size is presented in terms of value for the historic
year 2016 and estimated for forecast period of 2018-2024.
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The recent report identified that North America dominated the
global deep learning chipset market over the last couple of years, while
emerging markets of Asia-Pacific and Latin America are anticipated to
contribute significantly to the growth in the deep learning chipset market
worldwide.
The report segments the global deep learning chipset market on
the basis of Type, Compute Capacity and End-Users.
Global Deep Learning Chipset Market by Type
·
Graphics Processing Units
·
Central Processing Units
·
Application Specific Integrated Circuits
·
Field Programmable Gate Arrays
·
Others
Global Deep Learning Chipset Market by Compute
Capacity
·
Low (<1TFlops)
·
High (>1TFlops)
Global Deep Learning Chipset Market by End-Users
·
Consumer Electronics
·
Automotive
·
Industrial
·
Healthcare
·
Aerospace & Defense
·
Others
Companies Profiled in the Report
·
Google Inc.
·
Qualcomm
·
IBM Corporation
·
CEVA Inc.
·
Xilinx
·
Teradeep Inc.
·
Advanced Micro Devices
·
Graphcore
·
NVIDIA Corporation
·
Microsoft Corporation
About Infinium Global Research
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that provides its clients with market information services, solutions, and
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information, which are blended using forecasting models in order to project
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through which we determine various qualitative and quantitative parameters of
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