The global Deep Learning Chipset market was valued at 870.56 Million USD in 2017 and will reach 12044.99 Million USD by the end of 2025, growing at a CAGR of 38.88% during 2017-2025.
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.
Deep Learning Chipset can be divided into five categories: GPUs type, CPUs type, ASICs type, FPGAs type and other types. GPUs type accounted for the highest proportion in production market, with a figure of 79.68% in 2017, followed by CPUs type, account for 11.31% and ASICs type account for 6.56%. The chipset market today is led by graphics processing units (GPUs) and central processing units (CPUs), but during the coming years there will be an expanded role for other chipset types including application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and other emerging chipsets.
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.
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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.
This report focuses on top manufacturers in global market, Involved the assessment of Sales, price, revenue and market share for each manufacturer, covering
• NVIDIA
• Intel
• IBM
• Qualcomm
• CEVA
• KnuEdge
• AMD
• Xilinx
• ARM
• Google
• Graphcore
• TeraDeep
• Wave Computing
• BrainChip
On the basis of product, this report displays the Sales, revenue, price, market share and growth rate of each type, primarily split into
• Graphics Processing Units (GPUs)
• Central Processing Units (CPUs)
• Application Specific Integrated Circuits (ASICs)
• Field Programmable Gate Arrays (FPGAs)
• Others
By Application, this report focuses on Sales, Market share and Growth Rate of each application, can be divided into
• Consumer
• Aerospace, Military & Defense
• Automotive
• Industrial
• Medical
• Others
By Regions, this report splits global market into several key regions, with Sales, Revenue, Price and Gross Margin market share of top players in these regions, from 2014 to 2026 (forecast), like
• China
• USA
• Europe
• Japan
• Korea
• India
• Southeast Asia
• South America
Browse Full Research Report with TOC @ https://www.radiantinsights.com/research/global-deep-learning-chipset-market-2020-2026
Table of Contents
Global Deep Learning Chipset Market Professional Survey Report 2019
1 Report Overview
1.1 Definition and Specification
1.2 Manufacturers and Region Overview
1.2.1 Manufacturers Overview
1.2.2 Regions Overview
1.3 Type Overview
1.3.1 Graphics Processing Units (GPUs)
1.3.2 Central Processing Units (CPUs)
1.3.3 Application Specific Integrated Circuits (ASICs)
1.3.4 Field Programmable Gate Arrays (FPGAs)
1.3.5 Others
1.4 Application Overview
1.4.1 Consumer
1.4.2 Aerospace, Military & Defense
1.4.3 Automotive
1.4.4 Industrial
1.4.5 Medical
1.4.6 Others
1.5 Industrial Chain
1.5.1 Deep Learning Chipset Industrial Chain
1.5.2 Upstream
1.5.3 Downstream
2.1 The Overall Market Performance(Volume)
2.1.1 Graphics Processing Units (GPUs)
2.1.2 Central Processing Units (CPUs)
2.1.3 Application Specific Integrated Circuits (ASICs)
2.1.4 Field Programmable Gate Arrays (FPGAs)
2.1.5 Others
2.2 The Overall Market Performance(Value)
2.2.1 Graphics Processing Units (GPUs)
2.2.2 Central Processing Units (CPUs)
2.2.3 Application Specific Integrated Circuits (ASICs)
2.2.4 Field Programmable Gate Arrays (FPGAs)
2.2.5 Others
3 Global Deep Learning Chipset Market Assessment by Application
3.1 Overall Market Performance (Volume)
3.2 Consumer
3.3 Aerospace, Military & Defense
3.4 Automotive
3.5 Industrial
3.6 Medical
3.7 Others
4 Competitive Analysis
4.1 NVIDIA
4.1.1 NVIDIA Profiles
4.1.2 NVIDIA Product Information
4.1.3 NVIDIA Deep Learning Chipset Business Performance
4.1.4 SWOT Analysis
4.2 Intel
4.2.1 Intel Profiles
4.2.2 Intel Product Information
4.2.3 Intel Deep Learning Chipset Business Performance
4.2.4 SWOT Analysis
4.3 IBM
4.3.1 IBM Profiles
4.3.2 IBM Product Information
4.3.3 IBM Deep Learning Chipset Business Performance
4.3.4 SWOT Analysis
4.4 Qualcomm
4.4.1 Qualcomm Profiles
4.4.2 Qualcomm Product Information
4.4.3 Qualcomm Deep Learning Chipset Business Performance
4.4.4 SWOT Analysis
Continued…
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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.
Deep Learning Chipset can be divided into five categories: GPUs type, CPUs type, ASICs type, FPGAs type and other types. GPUs type accounted for the highest proportion in production market, with a figure of 79.68% in 2017, followed by CPUs type, account for 11.31% and ASICs type account for 6.56%. The chipset market today is led by graphics processing units (GPUs) and central processing units (CPUs), but during the coming years there will be an expanded role for other chipset types including application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and other emerging chipsets.
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.
Download Free Sample Report @ https://www.radiantinsights.com/research/global-deep-learning-chipset-market-2020-2026/request-sample
 |
| Deep Learning Chipset Market |
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.
This report focuses on top manufacturers in global market, Involved the assessment of Sales, price, revenue and market share for each manufacturer, covering
• NVIDIA
• Intel
• IBM
• Qualcomm
• CEVA
• KnuEdge
• AMD
• Xilinx
• ARM
• Graphcore
• TeraDeep
• Wave Computing
• BrainChip
On the basis of product, this report displays the Sales, revenue, price, market share and growth rate of each type, primarily split into
• Graphics Processing Units (GPUs)
• Central Processing Units (CPUs)
• Application Specific Integrated Circuits (ASICs)
• Field Programmable Gate Arrays (FPGAs)
• Others
By Application, this report focuses on Sales, Market share and Growth Rate of each application, can be divided into
• Consumer
• Aerospace, Military & Defense
• Automotive
• Industrial
• Medical
• Others
By Regions, this report splits global market into several key regions, with Sales, Revenue, Price and Gross Margin market share of top players in these regions, from 2014 to 2026 (forecast), like
• China
• USA
• Europe
• Japan
• Korea
• India
• Southeast Asia
• South America
Browse Full Research Report with TOC @ https://www.radiantinsights.com/research/global-deep-learning-chipset-market-2020-2026
Table of Contents
Global Deep Learning Chipset Market Professional Survey Report 2019
1 Report Overview
1.1 Definition and Specification
1.2 Manufacturers and Region Overview
1.2.1 Manufacturers Overview
1.2.2 Regions Overview
1.3 Type Overview
1.3.1 Graphics Processing Units (GPUs)
1.3.2 Central Processing Units (CPUs)
1.3.3 Application Specific Integrated Circuits (ASICs)
1.3.4 Field Programmable Gate Arrays (FPGAs)
1.3.5 Others
1.4 Application Overview
1.4.1 Consumer
1.4.2 Aerospace, Military & Defense
1.4.3 Automotive
1.4.4 Industrial
1.4.5 Medical
1.4.6 Others
1.5 Industrial Chain
1.5.1 Deep Learning Chipset Industrial Chain
1.5.2 Upstream
1.5.3 Downstream
2.1 The Overall Market Performance(Volume)
2.1.1 Graphics Processing Units (GPUs)
2.1.2 Central Processing Units (CPUs)
2.1.3 Application Specific Integrated Circuits (ASICs)
2.1.4 Field Programmable Gate Arrays (FPGAs)
2.1.5 Others
2.2 The Overall Market Performance(Value)
2.2.1 Graphics Processing Units (GPUs)
2.2.2 Central Processing Units (CPUs)
2.2.3 Application Specific Integrated Circuits (ASICs)
2.2.4 Field Programmable Gate Arrays (FPGAs)
2.2.5 Others
3 Global Deep Learning Chipset Market Assessment by Application
3.1 Overall Market Performance (Volume)
3.2 Consumer
3.3 Aerospace, Military & Defense
3.4 Automotive
3.5 Industrial
3.6 Medical
3.7 Others
4 Competitive Analysis
4.1 NVIDIA
4.1.1 NVIDIA Profiles
4.1.2 NVIDIA Product Information
4.1.3 NVIDIA Deep Learning Chipset Business Performance
4.1.4 SWOT Analysis
4.2 Intel
4.2.1 Intel Profiles
4.2.2 Intel Product Information
4.2.3 Intel Deep Learning Chipset Business Performance
4.2.4 SWOT Analysis
4.3 IBM
4.3.1 IBM Profiles
4.3.2 IBM Product Information
4.3.3 IBM Deep Learning Chipset Business Performance
4.3.4 SWOT Analysis
4.4 Qualcomm
4.4.1 Qualcomm Profiles
4.4.2 Qualcomm Product Information
4.4.3 Qualcomm Deep Learning Chipset Business Performance
4.4.4 SWOT Analysis
Continued…
Read all Reports of this category @ https://www.radiantinsights.com/catalog/machinery
About Radiant Insights
Radiant Insights is a platform for companies looking to meet their market research and business intelligence requirements. It assists and facilitate organizations and individuals procure market research reports, helping them in the decisions making process. The Organization has a comprehensive collection of reports, covering over 40 key industries and a host of micro markets. In addition to over extensive database of reports, experienced research coordinators also offer a host of ancillary services such as, research partnerships/ tie-ups and customized research solutions.
Media Contact:
Company Name: Radiant Insights, Inc
Contact Person: Michelle Thoras
Phone: (415) 349-0054
Toll Free: 1-888-928-9744
Address: 201 Spear Street 1100, Suite 3036,
City: San Francisco; State: California; Country: United States
Email: sales@radiantinsights.com
Website: https://www.radiantinsights.com/
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