Global and India Deep Learning Chipset Market research

Deep Learning Chipset Comprehensive Study by Type (Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Others), Application (Field Programmable Gate Arrays (FPGAs), Consumer, Aerospace, Military & Defense, Automotive, Industrial, Medical, Others), Compute Capacity (Low (<1TFlops), High (>1 TFlops)) Players and Region - Global Market Outlook to 2028

Deep Learning Chipset Market by XX Submarkets | Forecast Years 2023-2028  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
Market Snapshot:
Deep Learning Chipset technology has improved drastically in recent times owing to the growing demand of deep learning in industries to solve problems such as computer vision and patter recognition. Many semiconductors companies are coming up with new chipset technology to cater the demand and innovation fueled by heavy investment is underway in the very market.

Highlights from Deep Learning Chipset Market Study
AttributesDetails
Study Period2018-2028
Base Year2022
UnitValue (USD Million)


The key Players profiled in the report are NVIDIA [United States], Intel [United States], IBM [United States], Qualcomm [United States], CEVA [United Staes], KnuEdge [United States], AMD [United States], Xilinx [United States], ARM [United Kingdom], Google [United States], Graphcore [United Kingdom], TeraDeep [United States], Wave Computing [United States] and BrainChip [Australia]. Additionally, other players that are part of this comprehensive study are Huawei Technologies Co. Ltd [China], FinGenius Ltd. [United Kingdom], General Vision, Inc. [United States], MediaTek Inc [Taiwan], Samsung Electronics Co., Ltd [South Korea], Advanced Micro Devices [United States.] and Mellanox Technologies [Israel].

Geographic Breakdown and Segment Analysis
The Global Deep Learning Chipset market presents a comprehensive analysis of the Deep Learning Chipset market by product type (Graphics Processing Units (GPUs), Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) and Others), by end-user/application (Field Programmable Gate Arrays (FPGAs), Consumer, Aerospace, Military & Defense, Automotive, Industrial, Medical and Others), and by geography (North America, South America, Europe, Asia-Pacific and MEA) along with country level break-up. This section of our report presents a realistic picture of the Global Deep Learning Chipset industry. Investors and Players can easily understand the inherent opportunities and challenges for their products in geographical region of interest. For instance, while the holds majority of market share of the Deep Learning Chipset market

Analyst at AMA have segmented the market study of Global Deep Learning Chipset market by Type, Application and Region.

Influencing Trend:
Enhanced Computing Power and Reduced Hardware Cost and Integration Among Various Cloud Computing Service

Market Growth Drivers:
Growing Uses of Deep Learning in Big Data Analytics and Rising Cloud Based Technology

Challenges:
Lack of Flexibility and Multitasking

Restraints:
Lack of Technical Expertise

Opportunities:
Growing Use of Deep Learning in Consumer, Automotive, Medical and Aerospace Industries and Bringing Artificial Intelligence to Edge Devices

Market Developments Activities:
In September 2023, NVIDIA announced its acquisition of Mellanox Technologies. The acquisition will expand NVIDIA's data center market share and give the company a stronger position in the high-performance networking (HPCN) market. Mellanox's InfiniBand technology will be combined with NVIDIA's GPUs to create a more powerful and scalable platform for deep learning applications.
In October 2023,Google Cloud has launched TPUs v4, the latest iteration of its custom-designed Tensor Processing Units (TPUs), promising a 40% performance boost over its predecessor, TPU v3. This enhanced performance is attributed to new AI accelerators and memory architecture within the TPU v4 design.



The market is seeing moderate market players, by seeing huge growth in this market the key leading vendors are highly focusing on production technologies, efficiency enhancement, and product life. There is various growth opportunity in this market which is captured by leading players via tracking the ongoing process enhancement and huge investment in market growth strategies.

Key Target Audience
Semiconductor Companies, Technology Providers, System integrators, Universities and Research Organizations, Deep Learning Solution Providers, Deep Learning platform Providers, Cloud Service Providers, AI System Providers, Investors and Venture Capitalists and Others

Report Objectives / Segmentation Covered

By Type
  • Graphics Processing Units (GPUs)
  • Central Processing Units (CPUs)
  • Application Specific Integrated Circuits (ASICs)
  • Field Programmable Gate Arrays (FPGAs)
  • Others
By Application
  • Field Programmable Gate Arrays (FPGAs)
  • Consumer
  • Aerospace, Military & Defense
  • Automotive
  • Industrial
  • Medical
  • Others
By Compute Capacity
  • Low (<1TFlops)
  • High (>1 TFlops)

By Regions
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Taiwan
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • Italy
    • United Kingdom
    • Netherlands
    • Rest of Europe
  • MEA
    • Middle East
    • Africa
  • North America
    • United States
    • Canada
    • Mexico
  • 1. Market Overview
    • 1.1. Introduction
    • 1.2. Scope/Objective of the Study
      • 1.2.1. Research Objective
  • 2. Executive Summary
    • 2.1. Introduction
  • 3. Market Dynamics
    • 3.1. Introduction
    • 3.2. Market Drivers
      • 3.2.1. Growing Uses of Deep Learning in Big Data Analytics
      • 3.2.2. Rising Cloud Based Technology
    • 3.3. Market Challenges
      • 3.3.1. Lack of Flexibility and Multitasking
    • 3.4. Market Trends
      • 3.4.1. Enhanced Computing Power and Reduced Hardware Cost
      • 3.4.2. Integration Among Various Cloud Computing Service
  • 4. Market Factor Analysis
    • 4.1. Porters Five Forces
    • 4.2. Supply/Value Chain
    • 4.3. PESTEL analysis
    • 4.4. Market Entropy
    • 4.5. Patent/Trademark Analysis
  • 5. Global Deep Learning Chipset, by Type, Application, Compute Capacity and Region (value) (2017-2022)
    • 5.1. Introduction
    • 5.2. Global Deep Learning Chipset (Value)
      • 5.2.1. Global Deep Learning Chipset by: Type (Value)
        • 5.2.1.1. Graphics Processing Units (GPUs)
        • 5.2.1.2. Central Processing Units (CPUs)
        • 5.2.1.3. Application Specific Integrated Circuits (ASICs)
        • 5.2.1.4. Field Programmable Gate Arrays (FPGAs)
        • 5.2.1.5. Others
      • 5.2.2. Global Deep Learning Chipset by: Application (Value)
        • 5.2.2.1. Field Programmable Gate Arrays (FPGAs)
        • 5.2.2.2. Consumer
        • 5.2.2.3. Aerospace, Military & Defense
        • 5.2.2.4. Automotive
        • 5.2.2.5. Industrial
        • 5.2.2.6. Medical
        • 5.2.2.7. Others
      • 5.2.3. Global Deep Learning Chipset by: Compute Capacity (Value)
        • 5.2.3.1. Low (<1TFlops)
        • 5.2.3.2. High (>1 TFlops)
      • 5.2.4. Global Deep Learning Chipset Region
        • 5.2.4.1. South America
          • 5.2.4.1.1. Brazil
          • 5.2.4.1.2. Argentina
          • 5.2.4.1.3. Rest of South America
        • 5.2.4.2. Asia Pacific
          • 5.2.4.2.1. China
          • 5.2.4.2.2. Japan
          • 5.2.4.2.3. India
          • 5.2.4.2.4. South Korea
          • 5.2.4.2.5. Taiwan
          • 5.2.4.2.6. Australia
          • 5.2.4.2.7. Rest of Asia-Pacific
        • 5.2.4.3. Europe
          • 5.2.4.3.1. Germany
          • 5.2.4.3.2. France
          • 5.2.4.3.3. Italy
          • 5.2.4.3.4. United Kingdom
          • 5.2.4.3.5. Netherlands
          • 5.2.4.3.6. Rest of Europe
        • 5.2.4.4. MEA
          • 5.2.4.4.1. Middle East
          • 5.2.4.4.2. Africa
        • 5.2.4.5. North America
          • 5.2.4.5.1. United States
          • 5.2.4.5.2. Canada
          • 5.2.4.5.3. Mexico
  • 6. Deep Learning Chipset: Manufacturers/Players Analysis
    • 6.1. Competitive Landscape
      • 6.1.1. Market Share Analysis
        • 6.1.1.1. Top 3
        • 6.1.1.2. Top 5
    • 6.2. Peer Group Analysis (2022)
    • 6.3. BCG Matrix
    • 6.4. Company Profile
      • 6.4.1. NVIDIA [United States]
        • 6.4.1.1. Business Overview
        • 6.4.1.2. Products/Services Offerings
        • 6.4.1.3. Financial Analysis
        • 6.4.1.4. SWOT Analysis
      • 6.4.2. Intel [United States]
        • 6.4.2.1. Business Overview
        • 6.4.2.2. Products/Services Offerings
        • 6.4.2.3. Financial Analysis
        • 6.4.2.4. SWOT Analysis
      • 6.4.3. IBM [United States]
        • 6.4.3.1. Business Overview
        • 6.4.3.2. Products/Services Offerings
        • 6.4.3.3. Financial Analysis
        • 6.4.3.4. SWOT Analysis
      • 6.4.4. Qualcomm [United States]
        • 6.4.4.1. Business Overview
        • 6.4.4.2. Products/Services Offerings
        • 6.4.4.3. Financial Analysis
        • 6.4.4.4. SWOT Analysis
      • 6.4.5. CEVA [United Staes]
        • 6.4.5.1. Business Overview
        • 6.4.5.2. Products/Services Offerings
        • 6.4.5.3. Financial Analysis
        • 6.4.5.4. SWOT Analysis
      • 6.4.6. KnuEdge [United States]
        • 6.4.6.1. Business Overview
        • 6.4.6.2. Products/Services Offerings
        • 6.4.6.3. Financial Analysis
        • 6.4.6.4. SWOT Analysis
      • 6.4.7. AMD [United States]
        • 6.4.7.1. Business Overview
        • 6.4.7.2. Products/Services Offerings
        • 6.4.7.3. Financial Analysis
        • 6.4.7.4. SWOT Analysis
      • 6.4.8. Xilinx [United States]
        • 6.4.8.1. Business Overview
        • 6.4.8.2. Products/Services Offerings
        • 6.4.8.3. Financial Analysis
        • 6.4.8.4. SWOT Analysis
      • 6.4.9. ARM [United Kingdom]
        • 6.4.9.1. Business Overview
        • 6.4.9.2. Products/Services Offerings
        • 6.4.9.3. Financial Analysis
        • 6.4.9.4. SWOT Analysis
      • 6.4.10. Google [United States]
        • 6.4.10.1. Business Overview
        • 6.4.10.2. Products/Services Offerings
        • 6.4.10.3. Financial Analysis
        • 6.4.10.4. SWOT Analysis
      • 6.4.11. Graphcore [United Kingdom]
        • 6.4.11.1. Business Overview
        • 6.4.11.2. Products/Services Offerings
        • 6.4.11.3. Financial Analysis
        • 6.4.11.4. SWOT Analysis
      • 6.4.12. TeraDeep [United States]
        • 6.4.12.1. Business Overview
        • 6.4.12.2. Products/Services Offerings
        • 6.4.12.3. Financial Analysis
        • 6.4.12.4. SWOT Analysis
      • 6.4.13. Wave Computing [United States]
        • 6.4.13.1. Business Overview
        • 6.4.13.2. Products/Services Offerings
        • 6.4.13.3. Financial Analysis
        • 6.4.13.4. SWOT Analysis
      • 6.4.14. BrainChip [Australia]
        • 6.4.14.1. Business Overview
        • 6.4.14.2. Products/Services Offerings
        • 6.4.14.3. Financial Analysis
        • 6.4.14.4. SWOT Analysis
  • 7. Global Deep Learning Chipset Sale, by Type, Application, Compute Capacity and Region (value) (2023-2028)
    • 7.1. Introduction
    • 7.2. Global Deep Learning Chipset (Value)
      • 7.2.1. Global Deep Learning Chipset by: Type (Value)
        • 7.2.1.1. Graphics Processing Units (GPUs)
        • 7.2.1.2. Central Processing Units (CPUs)
        • 7.2.1.3. Application Specific Integrated Circuits (ASICs)
        • 7.2.1.4. Field Programmable Gate Arrays (FPGAs)
        • 7.2.1.5. Others
      • 7.2.2. Global Deep Learning Chipset by: Application (Value)
        • 7.2.2.1. Field Programmable Gate Arrays (FPGAs)
        • 7.2.2.2. Consumer
        • 7.2.2.3. Aerospace, Military & Defense
        • 7.2.2.4. Automotive
        • 7.2.2.5. Industrial
        • 7.2.2.6. Medical
        • 7.2.2.7. Others
      • 7.2.3. Global Deep Learning Chipset by: Compute Capacity (Value)
        • 7.2.3.1. Low (<1TFlops)
        • 7.2.3.2. High (>1 TFlops)
      • 7.2.4. Global Deep Learning Chipset Region
        • 7.2.4.1. South America
          • 7.2.4.1.1. Brazil
          • 7.2.4.1.2. Argentina
          • 7.2.4.1.3. Rest of South America
        • 7.2.4.2. Asia Pacific
          • 7.2.4.2.1. China
          • 7.2.4.2.2. Japan
          • 7.2.4.2.3. India
          • 7.2.4.2.4. South Korea
          • 7.2.4.2.5. Taiwan
          • 7.2.4.2.6. Australia
          • 7.2.4.2.7. Rest of Asia-Pacific
        • 7.2.4.3. Europe
          • 7.2.4.3.1. Germany
          • 7.2.4.3.2. France
          • 7.2.4.3.3. Italy
          • 7.2.4.3.4. United Kingdom
          • 7.2.4.3.5. Netherlands
          • 7.2.4.3.6. Rest of Europe
        • 7.2.4.4. MEA
          • 7.2.4.4.1. Middle East
          • 7.2.4.4.2. Africa
        • 7.2.4.5. North America
          • 7.2.4.5.1. United States
          • 7.2.4.5.2. Canada
          • 7.2.4.5.3. Mexico
  • 8. Appendix
    • 8.1. Acronyms
  • 9. Methodology and Data Source
    • 9.1. Methodology/Research Approach
      • 9.1.1. Research Programs/Design
      • 9.1.2. Market Size Estimation
      • 9.1.3. Market Breakdown and Data Triangulation
    • 9.2. Data Source
      • 9.2.1. Secondary Sources
      • 9.2.2. Primary Sources
    • 9.3. Disclaimer
List of Tables
  • Table 1. Deep Learning Chipset: by Type(USD Million)
  • Table 2. Deep Learning Chipset Graphics Processing Units (GPUs) , by Region USD Million (2017-2022)
  • Table 3. Deep Learning Chipset Central Processing Units (CPUs) , by Region USD Million (2017-2022)
  • Table 4. Deep Learning Chipset Application Specific Integrated Circuits (ASICs) , by Region USD Million (2017-2022)
  • Table 5. Deep Learning Chipset Field Programmable Gate Arrays (FPGAs) , by Region USD Million (2017-2022)
  • Table 6. Deep Learning Chipset Others , by Region USD Million (2017-2022)
  • Table 7. Deep Learning Chipset: by Application(USD Million)
  • Table 8. Deep Learning Chipset Field Programmable Gate Arrays (FPGAs) , by Region USD Million (2017-2022)
  • Table 9. Deep Learning Chipset Consumer , by Region USD Million (2017-2022)
  • Table 10. Deep Learning Chipset Aerospace, Military & Defense , by Region USD Million (2017-2022)
  • Table 11. Deep Learning Chipset Automotive , by Region USD Million (2017-2022)
  • Table 12. Deep Learning Chipset Industrial , by Region USD Million (2017-2022)
  • Table 13. Deep Learning Chipset Medical , by Region USD Million (2017-2022)
  • Table 14. Deep Learning Chipset Others , by Region USD Million (2017-2022)
  • Table 15. Deep Learning Chipset: by Compute Capacity(USD Million)
  • Table 16. Deep Learning Chipset Low (<1TFlops) , by Region USD Million (2017-2022)
  • Table 17. Deep Learning Chipset High (>1 TFlops) , by Region USD Million (2017-2022)
  • Table 18. South America Deep Learning Chipset, by Country USD Million (2017-2022)
  • Table 19. South America Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 20. South America Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 21. South America Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 22. Brazil Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 23. Brazil Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 24. Brazil Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 25. Argentina Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 26. Argentina Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 27. Argentina Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 28. Rest of South America Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 29. Rest of South America Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 30. Rest of South America Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 31. Asia Pacific Deep Learning Chipset, by Country USD Million (2017-2022)
  • Table 32. Asia Pacific Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 33. Asia Pacific Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 34. Asia Pacific Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 35. China Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 36. China Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 37. China Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 38. Japan Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 39. Japan Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 40. Japan Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 41. India Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 42. India Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 43. India Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 44. South Korea Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 45. South Korea Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 46. South Korea Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 47. Taiwan Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 48. Taiwan Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 49. Taiwan Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 50. Australia Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 51. Australia Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 52. Australia Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 53. Rest of Asia-Pacific Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 54. Rest of Asia-Pacific Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 55. Rest of Asia-Pacific Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 56. Europe Deep Learning Chipset, by Country USD Million (2017-2022)
  • Table 57. Europe Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 58. Europe Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 59. Europe Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 60. Germany Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 61. Germany Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 62. Germany Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 63. France Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 64. France Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 65. France Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 66. Italy Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 67. Italy Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 68. Italy Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 69. United Kingdom Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 70. United Kingdom Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 71. United Kingdom Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 72. Netherlands Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 73. Netherlands Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 74. Netherlands Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 75. Rest of Europe Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 76. Rest of Europe Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 77. Rest of Europe Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 78. MEA Deep Learning Chipset, by Country USD Million (2017-2022)
  • Table 79. MEA Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 80. MEA Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 81. MEA Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 82. Middle East Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 83. Middle East Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 84. Middle East Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 85. Africa Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 86. Africa Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 87. Africa Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 88. North America Deep Learning Chipset, by Country USD Million (2017-2022)
  • Table 89. North America Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 90. North America Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 91. North America Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 92. United States Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 93. United States Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 94. United States Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 95. Canada Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 96. Canada Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 97. Canada Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 98. Mexico Deep Learning Chipset, by Type USD Million (2017-2022)
  • Table 99. Mexico Deep Learning Chipset, by Application USD Million (2017-2022)
  • Table 100. Mexico Deep Learning Chipset, by Compute Capacity USD Million (2017-2022)
  • Table 101. Company Basic Information, Sales Area and Its Competitors
  • Table 102. Company Basic Information, Sales Area and Its Competitors
  • Table 103. Company Basic Information, Sales Area and Its Competitors
  • Table 104. Company Basic Information, Sales Area and Its Competitors
  • Table 105. Company Basic Information, Sales Area and Its Competitors
  • Table 106. Company Basic Information, Sales Area and Its Competitors
  • Table 107. Company Basic Information, Sales Area and Its Competitors
  • Table 108. Company Basic Information, Sales Area and Its Competitors
  • Table 109. Company Basic Information, Sales Area and Its Competitors
  • Table 110. Company Basic Information, Sales Area and Its Competitors
  • Table 111. Company Basic Information, Sales Area and Its Competitors
  • Table 112. Company Basic Information, Sales Area and Its Competitors
  • Table 113. Company Basic Information, Sales Area and Its Competitors
  • Table 114. Company Basic Information, Sales Area and Its Competitors
  • Table 115. Deep Learning Chipset: by Type(USD Million)
  • Table 116. Deep Learning Chipset Graphics Processing Units (GPUs) , by Region USD Million (2023-2028)
  • Table 117. Deep Learning Chipset Central Processing Units (CPUs) , by Region USD Million (2023-2028)
  • Table 118. Deep Learning Chipset Application Specific Integrated Circuits (ASICs) , by Region USD Million (2023-2028)
  • Table 119. Deep Learning Chipset Field Programmable Gate Arrays (FPGAs) , by Region USD Million (2023-2028)
  • Table 120. Deep Learning Chipset Others , by Region USD Million (2023-2028)
  • Table 121. Deep Learning Chipset: by Application(USD Million)
  • Table 122. Deep Learning Chipset Field Programmable Gate Arrays (FPGAs) , by Region USD Million (2023-2028)
  • Table 123. Deep Learning Chipset Consumer , by Region USD Million (2023-2028)
  • Table 124. Deep Learning Chipset Aerospace, Military & Defense , by Region USD Million (2023-2028)
  • Table 125. Deep Learning Chipset Automotive , by Region USD Million (2023-2028)
  • Table 126. Deep Learning Chipset Industrial , by Region USD Million (2023-2028)
  • Table 127. Deep Learning Chipset Medical , by Region USD Million (2023-2028)
  • Table 128. Deep Learning Chipset Others , by Region USD Million (2023-2028)
  • Table 129. Deep Learning Chipset: by Compute Capacity(USD Million)
  • Table 130. Deep Learning Chipset Low (<1TFlops) , by Region USD Million (2023-2028)
  • Table 131. Deep Learning Chipset High (>1 TFlops) , by Region USD Million (2023-2028)
  • Table 132. South America Deep Learning Chipset, by Country USD Million (2023-2028)
  • Table 133. South America Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 134. South America Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 135. South America Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 136. Brazil Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 137. Brazil Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 138. Brazil Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 139. Argentina Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 140. Argentina Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 141. Argentina Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 142. Rest of South America Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 143. Rest of South America Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 144. Rest of South America Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 145. Asia Pacific Deep Learning Chipset, by Country USD Million (2023-2028)
  • Table 146. Asia Pacific Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 147. Asia Pacific Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 148. Asia Pacific Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 149. China Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 150. China Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 151. China Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 152. Japan Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 153. Japan Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 154. Japan Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 155. India Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 156. India Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 157. India Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 158. South Korea Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 159. South Korea Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 160. South Korea Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 161. Taiwan Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 162. Taiwan Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 163. Taiwan Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 164. Australia Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 165. Australia Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 166. Australia Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 167. Rest of Asia-Pacific Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 168. Rest of Asia-Pacific Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 169. Rest of Asia-Pacific Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 170. Europe Deep Learning Chipset, by Country USD Million (2023-2028)
  • Table 171. Europe Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 172. Europe Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 173. Europe Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 174. Germany Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 175. Germany Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 176. Germany Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 177. France Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 178. France Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 179. France Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 180. Italy Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 181. Italy Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 182. Italy Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 183. United Kingdom Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 184. United Kingdom Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 185. United Kingdom Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 186. Netherlands Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 187. Netherlands Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 188. Netherlands Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 189. Rest of Europe Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 190. Rest of Europe Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 191. Rest of Europe Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 192. MEA Deep Learning Chipset, by Country USD Million (2023-2028)
  • Table 193. MEA Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 194. MEA Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 195. MEA Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 196. Middle East Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 197. Middle East Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 198. Middle East Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 199. Africa Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 200. Africa Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 201. Africa Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 202. North America Deep Learning Chipset, by Country USD Million (2023-2028)
  • Table 203. North America Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 204. North America Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 205. North America Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 206. United States Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 207. United States Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 208. United States Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 209. Canada Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 210. Canada Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 211. Canada Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 212. Mexico Deep Learning Chipset, by Type USD Million (2023-2028)
  • Table 213. Mexico Deep Learning Chipset, by Application USD Million (2023-2028)
  • Table 214. Mexico Deep Learning Chipset, by Compute Capacity USD Million (2023-2028)
  • Table 215. Research Programs/Design for This Report
  • Table 216. Key Data Information from Secondary Sources
  • Table 217. Key Data Information from Primary Sources
List of Figures
  • Figure 1. Porters Five Forces
  • Figure 2. Supply/Value Chain
  • Figure 3. PESTEL analysis
  • Figure 4. Global Deep Learning Chipset: by Type USD Million (2017-2022)
  • Figure 5. Global Deep Learning Chipset: by Application USD Million (2017-2022)
  • Figure 6. Global Deep Learning Chipset: by Compute Capacity USD Million (2017-2022)
  • Figure 7. South America Deep Learning Chipset Share (%), by Country
  • Figure 8. Asia Pacific Deep Learning Chipset Share (%), by Country
  • Figure 9. Europe Deep Learning Chipset Share (%), by Country
  • Figure 10. MEA Deep Learning Chipset Share (%), by Country
  • Figure 11. North America Deep Learning Chipset Share (%), by Country
  • Figure 12. Global Deep Learning Chipset share by Players 2022 (%)
  • Figure 13. Global Deep Learning Chipset share by Players (Top 3) 2022(%)
  • Figure 14. Global Deep Learning Chipset share by Players (Top 5) 2022(%)
  • Figure 15. BCG Matrix for key Companies
  • Figure 16. NVIDIA [United States] Revenue, Net Income and Gross profit
  • Figure 17. NVIDIA [United States] Revenue: by Geography 2022
  • Figure 18. Intel [United States] Revenue, Net Income and Gross profit
  • Figure 19. Intel [United States] Revenue: by Geography 2022
  • Figure 20. IBM [United States] Revenue, Net Income and Gross profit
  • Figure 21. IBM [United States] Revenue: by Geography 2022
  • Figure 22. Qualcomm [United States] Revenue, Net Income and Gross profit
  • Figure 23. Qualcomm [United States] Revenue: by Geography 2022
  • Figure 24. CEVA [United Staes] Revenue, Net Income and Gross profit
  • Figure 25. CEVA [United Staes] Revenue: by Geography 2022
  • Figure 26. KnuEdge [United States] Revenue, Net Income and Gross profit
  • Figure 27. KnuEdge [United States] Revenue: by Geography 2022
  • Figure 28. AMD [United States] Revenue, Net Income and Gross profit
  • Figure 29. AMD [United States] Revenue: by Geography 2022
  • Figure 30. Xilinx [United States] Revenue, Net Income and Gross profit
  • Figure 31. Xilinx [United States] Revenue: by Geography 2022
  • Figure 32. ARM [United Kingdom] Revenue, Net Income and Gross profit
  • Figure 33. ARM [United Kingdom] Revenue: by Geography 2022
  • Figure 34. Google [United States] Revenue, Net Income and Gross profit
  • Figure 35. Google [United States] Revenue: by Geography 2022
  • Figure 36. Graphcore [United Kingdom] Revenue, Net Income and Gross profit
  • Figure 37. Graphcore [United Kingdom] Revenue: by Geography 2022
  • Figure 38. TeraDeep [United States] Revenue, Net Income and Gross profit
  • Figure 39. TeraDeep [United States] Revenue: by Geography 2022
  • Figure 40. Wave Computing [United States] Revenue, Net Income and Gross profit
  • Figure 41. Wave Computing [United States] Revenue: by Geography 2022
  • Figure 42. BrainChip [Australia] Revenue, Net Income and Gross profit
  • Figure 43. BrainChip [Australia] Revenue: by Geography 2022
  • Figure 44. Global Deep Learning Chipset: by Type USD Million (2023-2028)
  • Figure 45. Global Deep Learning Chipset: by Application USD Million (2023-2028)
  • Figure 46. Global Deep Learning Chipset: by Compute Capacity USD Million (2023-2028)
  • Figure 47. South America Deep Learning Chipset Share (%), by Country
  • Figure 48. Asia Pacific Deep Learning Chipset Share (%), by Country
  • Figure 49. Europe Deep Learning Chipset Share (%), by Country
  • Figure 50. MEA Deep Learning Chipset Share (%), by Country
  • Figure 51. North America Deep Learning Chipset Share (%), by Country
List of companies from research coverage that are profiled in the study
  • NVIDIA [United States]
  • Intel [United States]
  • IBM [United States]
  • Qualcomm [United States]
  • CEVA [United Staes]
  • KnuEdge [United States]
  • AMD [United States]
  • Xilinx [United States]
  • ARM [United Kingdom]
  • Google [United States]
  • Graphcore [United Kingdom]
  • TeraDeep [United States]
  • Wave Computing [United States]
  • BrainChip [Australia]
Additional players considered in the study are as follows:
Huawei Technologies Co. Ltd [China] , FinGenius Ltd. [United Kingdom] , General Vision, Inc. [United States] , MediaTek Inc [Taiwan] , Samsung Electronics Co., Ltd [South Korea] , Advanced Micro Devices [United States.] , Mellanox Technologies [Israel]
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Dec 2023 217 Pages 66 Tables Base Year: 2022 Coverage: 15+ Companies; 18 Countries

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Global and India Deep Learning Chipset Market research Quantitative research analysis
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