Deep Learning Chipset Market: Introduction
Transparency Market Research delivers key insights on the global deep learning chipset market. In terms of revenue, the deep learning chipset market is estimated to expand at a CAGR of ~24% during the forecast period, owing to numerous factors, regarding which TMR offers thorough insights and forecasts in its report on the deep learning chipset market.
The deep learning technology is driving advancements in artificial intelligence (AI). In the current scenario, the deep learning chipset technology is being led by graphics processing units and central processing units. However, in the next few years, other chipset types including, application-specific integrated circuits and field programmable gate arrays, are expected to play an extended role.
Major Key Players of the Deep Learning Chipset Market are:
IBM Corporation, Graphcore Ltd, CEVA, Inc., Advanced Micro Devices, Inc., NVIDIA Corporation, Intel Corporation, IBM Corporation, Movidius, XILINX INC., TeraDeep Inc., QUALCOMM Incorporated, and Alphabet Inc.
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Deep Learning Chipset Market: Dynamics
While in the current scenario, a large number of developments are being witnessed in the robotics industry, the difference between human skills and robot motor skills still remains large. Machines still need to go a long way to match the human proficiency even at the level of basic sensorimotor skills such as grasping things. However, by linking learning with a continuous feedback, this gap can be bridged. By doing so, it would become easier for robots to understand the complexity of the current world and handle issues intelligently and reliably. The neural network technology has made great strides to design computer programs that can process images, text, and speech, and can draw pictures too. However, introducing various actions and control mechanism adds substantial new challenges to the network. Overcoming these challenges would help in understanding the method in which machines would communicate in the current ecosystem. By bringing the power of large-scale deep learning to the robotic control, fundamental issues in robotics and automation can be easily resolved. This, in turn, is expected to augment the deep learning chipset market during the forecast period.
Global Deep Learning Chipset Market: Segmentation
Deep Learning Chipset Market, by Type
- Graphics Processing Units (GPUs)
- Central Processing Units (CPUs)
- Application Specific Integrated Circuits (ASICs)
- Field Programmable Gate Arrays (FPGAs)
- Others
Deep Learning Chipset Market, by Compute Capacity
- High
- Low
Deep Learning Chipset Market, by End User
- Consumer Electronics
- Automotive
- Industrial
- Healthcare
- Aerospace & Defense
- Others
Deep Learning Chipset Market: Overview
- According to Transparency Market Research’s latest research report on the global deep learning chipset market for the historical period of 2017–2018 and the forecast period of 2019–2027, the rising demand for deep learning chipsets for use in various applications is expected to boost the deep learning chipset market during the forecast period
- In terms of revenue, the deep learning chipset market is estimated to reach a value of ~US$ 35.2 Bn by 2027, expanding at a CAGR of ~24% during the forecast period
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Research objectives:-
– To study and analyze the global Deep Learning Chipset consumption (value & volume) by key regions/countries, product type and application, history data.
– To understand the structure of the Deep Learning Chipset market by identifying its various sub-segments.
– Focuses on the key global Deep Learning Chipset manufacturers, to define, describe and analyze the sales volume, value, market share, market competitive landscape, SWOT analysis, and development plans in the next few years.
– To analyze the Deep Learning Chipset with respect to individual growth trends, future prospects, and their contribution to the total market.
– To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
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