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The Global Market for AI Chips 2024-2034

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  • Published: November 2023
  • Pages: 252
  • Tables: 53
  • Figures: 44
  • Series: Electronics

 

The speed of development of generative AI, boosted by the success of OpenAI's ChatGPT, is raising investor interest in companies working on AI-related infrastructure such as AI chips. Artificial Intelligence (AI) chips are a new generation of microprocessors chips designed to efficiently run AI-related workloads like machine learning, neural networks, and deep learning. As AI technology has advanced rapidly in recent years, there has been increasing demand for hardware optimized for AI processing versus general-purpose computer chips. AI chips are designed to run such AI algorithms faster and more efficiently than traditional processors. This has driven extensive research, development, and investment into AI chip technology by established and emerging companies.

The Global Market for AI Chips 2024-2034 provides a comprehensive analysis of the global AI chip landscape. Spanning over 300 pages, the report covers AI chip technology fundamentals, key capabilities enabled, applications across industries, market segmentation, regional trends, major players, start-up ecosystem, funding and investments, challenges, manufacturing and supply chain dynamics, architectural innovations, sustainability impacts, and the future outlook for these transformative technologies.

Multiple data tables and charts quantify market size projections to 2034 by region, vertical, chip type, and more. Profiles of over 100 companies highlight competitive positioning. Expert insights identify growth opportunities as specialized AI hardware progresses. The Global Market for AI Chips 2024-2034 is ideal for semiconductor industry participants, tech investors, and companies strategizing AI chip adoption to inform planning amid this rapidly evolving space.

Report contents include:

  • AI Chip Technology Fundamentals
    • Architectures like GPUs, ASICs, neuromorphic chips
    • Processing capabilities enabled by AI hardware
    • Development history and ecosystem
  • Market Landscape and Segmentation
    • Market size forecasts globally and by region
    • Breakdown by chip type - ASICs, GPUs, CPUs, FPGAs
    • Split by training vs inference workloads
    • Segmentation by end-use industry vertical
  • Regional Analysis
    • AI chip development trends in China
    • Government policies in the US, Europe, South Korea, Japan
    • Edge AI advances by country
  • Industry Drivers and Adoption Factors
    • Key market growth drivers
    • Government funding and R&D initiatives
    • Corporate investments fuelling innovation
    • Applications propelling demand across domains
  • Competitive Environment
    • Profiles of over 130 leading companies. Companies profiled include AMD, Astrus, Celestial AI, Cerebras, d-Matrix, DEEPX, EdgeCortix® Inc., Etched.ai, Enfabrica, Enflame, Google, Horizon Robotics, IBM, Kneron, Lightmatter, Modular, MediaTek Inc, Mythic, Neuchips, Nvidia, Panmnesia, Rebellions, Samsung, SambaNova Systems, Sapeon, SiMa.ai, SpiNNcloud Systems GmbH and Tenstorrent. 
    • Start-ups advancing new architectures
    • Silicon giants leveraging semiconductor expertise
    • Cloud providers and automotive supplier activity
  • Technology Innovations
    • Novel materials, packaging, software abstractions
    • Architectural advances in processing, memory, interconnects
    • Progress in manufacturing techniques like lithography, 3D stacking
  • Challenges and Sustainability
    • Design, benchmarking, programming complexities
    • Geopolitical implications and policy considerations
    • Environmental stewardship priorities and frameworks

 

The Global Market for AI Chips 2024-2034
The Global Market for AI Chips 2024-2034
PDF download/by email.
Price: £1,200.00

The Global Market for AI Chips 2024-2034
The Global Market for AI Chips 2024-2034
PDF and Hard Copy (price includes tracked FEDEX delivery)
Price: £1,250.00

Payment methods: Visa, Mastercard, American Express, Paypal, Bank Transfer. 

To purchase by invoice (bank transfer) contact info@futuremarketsinc.com or select Bank Transfer (Invoice) as a payment method at checkout.

 

 

1              RESEARCH METHODOLOGY         15

 

2              INTRODUCTION 16

  • 2.1          What is an AI chip?          16
    • 2.1.1      AI Acceleration  17
    • 2.1.2      Hardware & Software Co-Design               17
  • 2.2          Key capabilities 18
  • 2.3          History of AI Chip Development 19
  • 2.4          Applications       20
  • 2.5          AI Chip Architectures      21
  • 2.6          Computing requirements             23
  • 2.7          Semiconductor packaging             24
    • 2.7.1      Evolution from 1D to 3D semiconductor packaging            24
  • 2.8          AI chip market landscape              25
    • 2.8.1      China     26
    • 2.8.2      USA       28
      • 2.8.2.1   The US CHIPS and Science Act of 2022     28
    • 2.8.3      Europe 29
      • 2.8.3.1   The European Chips Act of 2022 29
    • 2.8.4      Rest of Asia         30
      • 2.8.4.1   South Korea       30
      • 2.8.4.2   Japan    30
      • 2.8.4.3   Taiwan  31
  • 2.9          Edge AI 31
    • 2.9.1      Edge vs Cloud    32
    • 2.9.2      Edge devices that utilize AI chips               33
    • 2.9.3      Players in edge AI chips 34
    • 2.9.4      Inference at the edge     35
  • 2.10        Market drivers  36
  • 2.11        Government funding and initiatives         37
  • 2.12        Funding and investments             38
  • 2.13        Market challenges           41
  • 2.14        Market players  43
  • 2.15        Future Outlook for AI Chips          44
    • 2.15.1    Specialization     44
    • 2.15.2    3D System Integration   45
    • 2.15.3    Software Abstraction Layers        45
    • 2.15.4    Edge-Cloud Convergence             45
    • 2.15.5    Environmental Sustainability       45
    • 2.15.6    Neuromorphic Photonics              45
    • 2.15.7    New Materials   46
    • 2.15.8    Efficiency Improvements              47
    • 2.15.9    Automated Chip Generation       48
  • 2.16        AI roadmap         49

 

3              AI CHIP FABRICATION    50

  • 3.1          Supply chain       50
  • 3.2          Fab investments and capabilities               51
  • 3.3          Manufacturing advances              53
    • 3.3.1      Chiplets                53
    • 3.3.2      3D Fabrication   53
    • 3.3.3      Algorithm-Hardware Co-Design 54
    • 3.3.4      Advanced Lithography   54
    • 3.3.5      Novel Devices    55

 

4              AI CHIP ARCHITECTURES               56

  • 4.1          Distributed Parallel Processing   56
  • 4.2          Optimized Data Flow      56
  • 4.3          Flexible vs. Specialized Designs  57
  • 4.4          Hardware for Training vs. Inference         58
  • 4.5          Software Programmability           58
  • 4.6          Architectural Optimization Goals               59
  • 4.7          Innovations        60
    • 4.7.1      Specialized Processing Units        60
    • 4.7.2      Dataflow Optimization   61
    • 4.7.3      Model Compression       61
    • 4.7.4      Biologically-Inspired Designs       62
    • 4.7.5      Analog Computing           63
    • 4.7.6      Photonic Connectivity    63
  • 4.8          Sustainability     64
    • 4.8.1      Energy Efficiency              64
    • 4.8.2      Green Data Centers        64
    • 4.8.3      Eco-Electronics  65
    • 4.8.4      Reusable Architectures & IP         65
    • 4.8.5      Regulated Lifecycles       65
    • 4.8.6      AI for Sustainability         66
    • 4.8.7      AI Model Efficiency         66
  • 4.9          Companies, by architecture         67

 

5              TYPES OF AI CHIPS           68

  • 5.1          Training Accelerators     68
  • 5.2          Inference Accelerators  70
  • 5.3          Automotive AI Chips       72
  • 5.4          Smart Device AI Chips    74
  • 5.5          Cloud Data Center Chips               76
  • 5.6          Edge AI Chips     78
  • 5.7          Neuromorphic Chips       79
  • 5.8          FPGA-Based Solutions   80
  • 5.9          Multi-Chip Modules        81
  • 5.10        Emerging technologies  83
    • 5.10.1    Novel Materials 83
      • 5.10.1.1                2D materials       83
      • 5.10.1.2                Photonic materials           84
      • 5.10.1.3                Spintronic materials        84
      • 5.10.1.4                Phase change materials 85
      • 5.10.1.5                Neuromorphic materials               86
    • 5.10.2    Advanced Packaging       86
    • 5.10.3    Software Abstraction     87
    • 5.10.4    Environmental Sustainability       87
  • 5.11        Specialized components               88
    • 5.11.1    Sensor Interfacing           88
    • 5.11.2    Memory Technologies   89
      • 5.11.2.1                HBM stacks         89
      • 5.11.2.2                GDDR    89
      • 5.11.2.3                SRAM    90
      • 5.11.2.4                STT-RAM             90
      • 5.11.2.5                ReRAM 90
    • 5.11.3    Software Frameworks   90
    • 5.11.4    Data Center Design         91

 

6              AI CHIP MARKETS             93

  • 6.1          Market map       93
  • 6.2          Data Centers      95
    • 6.2.1      Market overview             95
    • 6.2.2      Market players  95
    • 6.2.3      Hardware            96
    • 6.2.4      Trends  96
  • 6.3          Automotive        98
    • 6.3.1      Market overview             98
    • 6.3.2      Market outlook 98
    • 6.3.3      Autonomous Driving       99
      • 6.3.3.1   Market players  99
    • 6.3.4      Increasing power demands          100
    • 6.3.5      Market players  101
  • 6.4          Industry 4.0        102
    • 6.4.1      Market overview             102
    • 6.4.2      Applications       102
    • 6.4.3      Market players  103
  • 6.5          Smartphones     104
    • 6.5.1      Market overview             104
    • 6.5.2      Commercial examples   106
    • 6.5.3      Smartphone chipset market        107
    • 6.5.4      Process nodes   107
  • 6.6          Tablets 109
    • 6.6.1      Market overview             109
    • 6.6.2      Market players  109
  • 6.7          IoT & IIoT             111
    • 6.7.1      Market overview             111
    • 6.7.2      AI on the IoT edge           111
    • 6.7.3      Consumer smart appliances         112
    • 6.7.4      Market players  113
  • 6.8          Computing          114
    • 6.8.1      Market overview             114
    • 6.8.2      Personal computers        114
    • 6.8.3      Parallel computing          115
    • 6.8.4      Low-precision computing             115
    • 6.8.5      Market players  116
  • 6.9          Drones & Robotics           117
    • 6.9.1      Market overview             117
    • 6.9.2      Market players  118
  • 6.10        Wearables, AR glasses and hearables      119
    • 6.10.1    Market overview             119
    • 6.10.2    Applications       119
    • 6.10.3    Market players  120
  • 6.11        Sensors 122
    • 6.11.1    Market overview             122
    • 6.11.2    Challenges          122
    • 6.11.3    Applications       123
    • 6.11.4    Market players  123
  • 6.12        Life Sciences      125
    • 6.12.1    Market overview             125
    • 6.12.2    Applications       125
    • 6.12.3    Market players  126

 

7              GLOBAL MARKET REVENUES AND COSTS                127

  • 7.1          Costs     127
  • 7.2          Revenues by chip type, 2020-2034             128
  • 7.3          Revenues by market, 2020-2034                130
  • 7.4          Revenues by region, 2020-2034  132

 

8              COMPANY PROFILES       134 (133 company profiles)

 

9              REFERENCES       249

 

List of Tables

  • Table 1. Markets and applications for AI chips.    21
  • Table 2. AI Chip Architectures.   22
  • Table 3. Computing requirements and constraints.           23
  • Table 4. Computing requirements and constraints by applications.             23
  • Table 5. Advantages and disadvantages of edge AI.           31
  • Table 6. Edge vs Cloud.  32
  • Table 7. Edge devices that utilize AI chips.             33
  • Table 8. Players in edge AI chips.               35
  • Table 9. Market drivers for AI Chips.        36
  • Table 10. AI chip government funding and initiatives.       37
  • Table 11. AI chips funding and investment, by company. 38
  • Table 12. Market challenges in AI chips. 42
  • Table 13. Key players in AI chips.               43
  • Table 14. AI Chip Supply Chain.  50
  • Table 15. Fab investments and capabilities.          52
  • Table 16. Comparison of AI chip fabrication capabilities between IDMs (integrated device manufacturers) and dedicated foundries.      52
  • Table 17. Goals driving the exploration into AI chip architectures.              59
  • Table 18. Concepts from neuroscience influence architecture.     62
  • Table 19. Companies by Architecture.     67
  • Table 20. Types of training accelerators for AI chips.         70
  • Table 21. Types of inference accelerators for AI chips.      72
  • Table 22. Types of Automotive AI chips. 74
  • Table 23. Smart device AI chips. 76
  • Table 24.  Types of cloud data center AI chips.     77
  • Table 25. Key types of edge AI chips.        78
  • Table 26. Types of neuromorphic chips and their attributes.         80
  • Table 27. Types of FPGA-based solutions for AI acceleration.        81
  • Table 28. Types of multi-chip module (MCM) integration approaches for AI chips.               82
  • Table 29. 2D materials in AI hardware.   83
  • Table 30. Photonic materials for AI hardware.     84
  • Table 31. Spintronic materials for AI hardware.   84
  • Table 32.  Phase change materials for AI hardware.           85
  • Table 33. Neuromorphic materials in AI hardware.            86
  • Table 34. Techniques for combining chiplets and dies using advanced packaging for AI chips.         86
  • Table 35. Types of sensors.          88
  • Table 36. Key AI chip products and solutions targeting automotive applications.  99
  • Table 37. AI versus non-AI smartphones 104
  • Table 38. Key chip fabrication process nodes used by various mobile AI chip designers.    108
  • Table 39. AI versus non AI tablets.            110
  • Table 40. Market players in AI chips for personal, parallel, and low-precision computing. 116
  • Table 41. AI chip company products for drones and robotics.        118
  • Table 42.  Applications of AI chips in wearable devices.   120
  • Table 43. Applications of ai chips and sensors and structural health monitoring.   123
  • Table 44. Applications of AI chips in life sciences.               125
  • Table 45. AI chip costs analysis-design, operation and fabrication.              127
  • Table 46. Design, manufacturing, testing, and operational costs associated with leading-edge process nodes for AI chips.    127
  • Table 47. Assembly, test, and packaging (ATP) costs associated with manufacturing AI chips.          128
  • Table 48. Global market revenues by chip type, 2020-2034 (billions USD).              129
  • Table 49. Global market revenues by market, 2020-2034 (billions USD).  130
  • Table 50. Global market revenues by region, 2020-2034 (billions USD).    132
  • Table 51. AMD AI chip range.      136
  • Table 52. Applications of CV3-AD685 in autonomous driving.        141
  • Table 53. Evolution of Apple Neural Engine.         144

 

List of Figures

  • Figure 1. Nvidia H200 AI Chip.     16
  • Figure 2. History of AI development.       19
  • Figure 3. AI roadmap.     49
  • Figure 4. Nvidia A100 GPU .         68
  • Figure 5. Google Cloud TPUs.      69
  • Figure 6. Groq Node.      69
  • Figure 7. Intel Movidius Myriad X.             71
  • Figure 8. Qualcomm Cloud AI 100.             72
  • Figure 9. Tesla FSD Chip.               73
  • Figure 10. Qualcomm Snapdragon.           75
  • Figure 11. AI chio market map.   94
  • Figure 12. Global market revenues by chip type, 2020-2034 (billions USD).             130
  • Figure 13. Global market revenues by market 2020-2034 (billions USD).  131
  • Figure 14. Global market revenues by region, 2020-2034 (billions USD).  133
  • Figure 15. AMD Radeon Instinct.               137
  • Figure 16. AMD Ryzen 7040.        137
  • Figure 17. Alveo V70.      137
  • Figure 18. Versal Adaptive SOC. 138
  • Figure 19. AMD’s MI300 chip.     138
  • Figure 20. Cerebas WSE-2.           155
  • Figure 21. DeepX NPU DX-GEN1.               161
  • Figure 22. InferX X1.       170
  • Figure 23. “Warboy”(AI Inference Chip). 171
  • Figure 24. Google TPU.  172
  • Figure 25. GrAI VIP.         173
  • Figure 26. Colossus™ MK2 GC200 IPU.    175
  • Figure 27. GreenWave’s GAP8 and GAP9 processors.       176
  • Figure 28. Journey 5.      180
  • Figure 29. IBM Telum processor.               183
  • Figure 30. 11th Gen Intel® Core™ S-Series.           186
  • Figure 31. Envise.             194
  • Figure 32. Pentonic 2000.              198
  • Figure 33. Meta Training and Inference Accelerator (MTIA).          199
  • Figure 34. Azure Maia 100 and Cobalt 100 chips. 201
  • Figure 35. Mythic MP10304 Quad-AMP PCIe Card.            205
  • Figure 36. Nvidia H200 AI chip.   214
  • Figure 37. Grace Hopper Superchip.         215
  • Figure 38. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below).                217
  • Figure 39. Cloud AI 100. 220
  • Figure 40. Peta Op chip. 223
  • Figure 41. Cardinal SN10 RDU.    226
  • Figure 42. MLSoC™.        231
  • Figure 43. Grayskull.       237
  • Figure 44. Tesla D1 chip.                238

 

      

The Global Market for AI Chips 2024-2034
The Global Market for AI Chips 2024-2034
PDF download/by email.
Price: £1,200.00

The Global Market for AI Chips 2024-2034
The Global Market for AI Chips 2024-2034
PDF and Hard Copy (price includes tracked FEDEX delivery)
Price: £1,250.00

Payment methods: Visa, Mastercard, American Express, Paypal, Bank Transfer. 

To purchase by invoice (bank transfer) contact info@futuremarketsinc.com or select Bank Transfer (Invoice) as a payment method at checkout.

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