The Global Market for AI Chips 2024-2034

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

PDF download/by email.

The Global Market for AI Chips 2024-2034

PDF and Hard Copy (price includes tracked FEDEX delivery)

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