The global artificial intelligence (AI) chips market report 2026-2036 from Future Markets Inc provides comprehensive strategic intelligence on the most consequential semiconductor category of the decade. AI silicon — spanning data centre training and inference accelerators, edge AI processors, and on-device neural engines — has become the single largest driver of global semiconductor capital expenditure, with NVIDIA’s GPU dominance, hyperscaler custom ASIC programmes and a wave of dedicated AI chip start-ups simultaneously reshaping the competitive landscape.
Global AI Chips Market Report 2026-2036 — Key Coverage Areas
- Data Centre Training Accelerators — NVIDIA Hopper, Blackwell and Rubin generations, AMD Instinct MI300/MI350, Intel Gaudi, and the architectural evolution of training silicon
- Hyperscaler Custom ASICs — Google TPU, AWS Trainium and Inferentia, Microsoft Maia, Meta MTIA, and the in-house silicon strategies reshaping the competitive landscape
- Inference and Edge AI — dedicated inference accelerators, automotive AI SoCs, smartphone NPUs, IoT AI chips, and the diverging design priorities for inference workloads
- Dedicated AI Start-Ups — Cerebras, Groq, SambaNova, Tenstorrent, Graphcore, Etched, Rebellions, MatX and the venture-backed cohort competing on architectural differentiation
- Memory and Packaging — HBM3E and HBM4 supply allocation, advanced packaging including CoWoS and glass substrates, co-packaged optics, and the memory bottleneck shaping silicon roadmaps
- Geopolitics and Supply Chain — US export controls, China’s domestic AI chip ecosystem (Huawei Ascend, Cambricon, Biren), TSMC and Samsung foundry capacity allocation, and the strategic supply chain dynamics
- Software and Ecosystem — CUDA dominance, ROCm and other open alternatives, MLIR, ONNX, and the software moat shaping competitive positioning
- 10-Year Forecasts — unit shipments, market value, segment-level demand and capital deployment forecasts by chip category, application and region from 2026 to 2036
Ideal for semiconductor IDMs, fabless designers and foundries, hyperscaler procurement teams, system OEMs, memory and advanced packaging suppliers, software and tooling vendors, and investors tracking AI semiconductor competition.
cover
- Published: September 2025
- Pages: 311
- Tables: 69
- Figures: 48
The global AI chip market is experiencing unprecedented growth in 2025. The first quarter of 2025 demonstrated the market's robust health with 75 startups collectively raising over $2 billion. AI chips and enabling technologies emerged as major winners, with companies developing optical communications technology for chips and data center infrastructure pulling in over $400 million. Notably, six companies raised at least $100 million in investment during Q1 alone. Recent funding rounds throughout 2024-2025 reveal sustained investor confidence across diverse AI chip technologies. Major European investments include VSORA's $46 million raise led by Otium for high-performance AI inference chips, and Axelera AI's €61.6 million grant from the EuroHPC Joint Undertaking for RISC-V-based AI acceleration platforms. Asian markets showed strong momentum with Rebellions securing $124 million in Series B funding led by KT Corp for domain-specific AI processors, while HyperAccel raised $40 million for generative AI inference solutions.
Emerging technologies attracted significant capital, particularly in neuromorphic computing and analog processing. Innatera Nanosystems raised €15 million for brain-inspired processors using spiking neural networks, while Semron secured €7.3 million for analog in-memory computing using memcapacitors. These investments highlight the industry's push toward ultra-low power edge AI solutions.
Optical and photonic technologies dominated large funding rounds, with Celestial AI raising $250.0M in Series C1 funding led by Fidelity Management & Research Company for photonic fabric technology. Similarly, quantum computing platforms attracted substantial investment, including QuEra Computing's $230.0M financing from Google and SoftBank Vision Fund for neutral-atom quantum computers. Government support continued expanding globally, with Japan's NEDO providing significant subsidies including EdgeCortix's combined $46.7 million in government funding for AI chiplet development. European initiatives showed strong momentum through the European Innovation Council Fund's participation in multiple rounds, supporting companies like NeuReality ($20 million) and CogniFiber ($5 million).
North American companies maintained strong fundraising activity, with Etched raising $120 million for transformer-specific ASICs and Groq securing $640 million in Series D funding for language processing units. Tenstorrent's massive $693 million Series D round, led by Samsung Securities, demonstrated continued confidence in RISC-V-based AI processor IP. The sustained investment flows reflect fundamental shifts in AI computing requirements. Industry analysts project that the market for gen AI inference will grow faster than training in 2025 and beyond, driving demand for specialized inference accelerators. Companies like Recogni ($102 million), SiMa.ai ($70 million), and Blaize ($106 million) received substantial funding specifically for inference-optimized solutions.
Edge computing represents a critical growth vector, with companies developing ultra-low power solutions attracting significant investment. Blumind's $14.1 million raise for analog AI inference chips and Mobilint's $15.3 million Series B for edge NPU chips demonstrate investor recognition of the edge AI opportunity.
The competitive landscape continues evolving with new architectural approaches gaining traction. Fractile's $15 million seed funding for in-memory processing chips and Vaire Computing's $4.5 million raise for adiabatic reversible computing represent novel approaches to addressing AI's energy consumption challenges.
AI chip startups secured a cumulative US$7.6 billion in venture capital funding globally during the second, third, and last quarter of 2024, with 2025 maintaining this momentum across diverse technology categories, from photonic interconnects to neuromorphic processors, positioning the industry for continued rapid expansion and technological innovation.
Data center and cloud infrastructure represent the primary growth drivers. Chip sales are set to soar in 2025, led by generative AI and data center build-outs, even as traditional PC and mobile markets remain subdued. The investment focus reflects this trend, with optical interconnect and photonic technologies receiving substantial attention from venture capitalists and strategic investors. Government funding has become increasingly strategic, with governments around the globe starting to invest more heavily in chip design tools and related research as part of an effort to boost on-shore chip production.
The Global Artificial Intelligence (AI) Chips Market 2026-2036 provides comprehensive analysis of the rapidly evolving AI semiconductor industry, covering market dynamics, technological innovations, competitive landscapes, and future growth opportunities across multiple application sectors. This strategic market intelligence report examines the complete AI chip ecosystem from emerging neuromorphic processors to established GPU architectures, delivering critical insights for semiconductor manufacturers, technology investors, system integrators, and enterprise decision-makers navigating the AI revolution.
Report contents include:
- Market size forecasts and revenue projections by chip type, application, and region (2026-2036)
- Technology readiness levels and commercialization timelines for next-generation AI accelerators
- Competitive analysis of 140+ companies including NVIDIA, AMD, Intel, Google, Amazon, and emerging AI chip startups
- Supply chain analysis covering fab investments, advanced packaging technologies, and manufacturing capabilities
- Government funding initiatives and policy impacts across US, Europe, China, and Asia-Pacific regions
- Edge AI vs. cloud computing trends and architectural requirements
- AI Chip Definition & Core Technologies - Hardware acceleration principles, software co-design methodologies, and key performance capabilities
- Historical Development Analysis - Evolution from general-purpose processors to specialized AI accelerators and neuromorphic computing
- Application Landscape - Comprehensive coverage of data centers, automotive, smartphones, IoT, robotics, and emerging use cases
- Architectural Classifications - Training vs. inference optimizations, edge vs. cloud requirements, and power efficiency considerations
- Computing Requirements Analysis - Memory bandwidth, processing throughput, and latency specifications across different AI workloads
- Semiconductor Packaging Evolution - 1D to 3D integration technologies, chiplet architectures, and advanced packaging solutions
- Regional Market Dynamics - China's domestic chip initiatives, US CHIPS Act implications, European Chips Act strategic goals, and Asia-Pacific manufacturing hubs
- Edge AI Deployment Strategies - Edge vs. cloud trade-offs, inference optimization, and distributed AI architectures
- AI Chip Fabrication & Technology Infrastructure
- Supply Chain Ecosystem - Foundry capabilities, IDM strategies, and manufacturing bottlenecks analysis
- Fab Investment Trends - Capital expenditure analysis, capacity expansion plans, and technology node roadmaps
- Manufacturing Innovations - Chiplet integration, 3D fabrication techniques, algorithm-hardware co-design, and advanced lithography
- Instruction Set Architectures - RISC vs. CISC implementations for AI workloads and specialized ISA developments
- Programming & Execution Models - Von Neumann architecture limitations and alternative computing paradigms
- Transistor Technology Roadmap - FinFET scaling, GAAFET transitions, and next-generation device architectures
- Advanced Packaging Technologies - 2.5D packaging implementations, heterogeneous integration, and system-in-package solutions
- AI Chip Architectures & Design Innovations
- Distributed Parallel Processing - Multi-core architectures, interconnect technologies, and scalability solutions
- Optimized Data Flow Architectures - Memory hierarchy optimization, data movement minimization, and bandwidth enhancement
- Design Flexibility Analysis - Specialized vs. general-purpose trade-offs and programmability requirements
- Training vs. Inference Hardware - Architectural differences, precision requirements, and performance optimization strategies
- Software Programmability Frameworks - Development tools, compiler optimizations, and deployment ecosystems
- Architectural Innovation Trends - Specialized processing units, dataflow optimization, model compression techniques
- Biologically-Inspired Designs - Neuromorphic computing principles and spike-based processing architectures
- Analog Computing Revival - Mixed-signal processing, in-memory computing, and energy efficiency benefits
- Photonic Connectivity Solutions - Optical interconnects, silicon photonics integration, and bandwidth scaling
- Sustainability Considerations - Energy efficiency metrics, green data center requirements, and lifecycle management
- Comprehensive AI Chip Type Analysis
- Training Accelerators - High-performance computing requirements, multi-GPU scaling, and distributed training architectures
- Inference Accelerators - Real-time processing optimization, edge deployment considerations, and latency minimization
- Automotive AI Chips - ADAS implementations, autonomous driving processors, and safety-critical system requirements
- Smart Device AI Chips - Mobile processors, power efficiency optimization, and on-device AI capabilities
- Cloud Data Center Chips - Hyperscale deployment strategies, rack-level optimization, and cooling considerations
- Edge AI Chips - Power-constrained environments, real-time processing, and connectivity requirements
- Neuromorphic Chips - Brain-inspired architectures, spike-based processing, and ultra-low power applications
- FPGA-Based Solutions - Reconfigurable computing, rapid prototyping, and application-specific optimization
- Multi-Chip Modules - Heterogeneous integration strategies, chiplet ecosystems, and system-level optimization
- Emerging Technologies - Novel materials (2D, photonic, spintronic), advanced packaging, and next-generation computing paradigms
- Memory Technologies - HBM stacks, GDDR implementations, SRAM optimization, and emerging memory solutions
- CPU Integration - AI acceleration in general-purpose processors and hybrid computing architectures
- GPU Evolution - Data center GPU trends, NVIDIA ecosystem analysis, AMD competitive positioning, and Intel market entry
- Custom ASIC Development - Cloud service provider strategies, Amazon Trainium/Inferentia, Microsoft Maia, Meta MTIA analysis
- Alternative Architectures - Spatial accelerators, CGRAs, and heterogeneous matrix-based solutions
- Market Applications & Vertical Analysis
- Data Center Market - Hyperscale deployment trends, cloud infrastructure requirements, and performance benchmarking
- Automotive Sector - Autonomous driving chip requirements, power management, and safety certification processes
- Industry 4.0 Applications - Smart manufacturing, predictive maintenance, and industrial automation use cases
- Smartphone Integration - Mobile AI processor evolution, performance improvements, and competitive landscape
- Tablet Computing - AI acceleration in consumer devices and productivity applications
- IoT & Industrial IoT - Edge computing requirements, sensor integration, and connectivity solutions
- Personal Computing - AI-enabled laptops, desktop acceleration, and parallel computing applications
- Drones & Robotics - Real-time processing requirements, power constraints, and autonomous operation capabilities
- Wearables & AR/VR - Ultra-low power AI, gesture recognition, and immersive computing applications
- Sensor Applications - Smart sensors, structural health monitoring, and distributed sensing networks
- Life Sciences - Medical imaging acceleration, drug discovery applications, and diagnostic AI systems
- Financial Analysis & Market Forecasts
- Cost Structure Analysis - Design, manufacturing, testing, and operational cost breakdowns across technology nodes
- Revenue Projections by Chip Type - Market size forecasts segmented by GPU, ASIC, FPGA, and emerging technologies (2020-2036)
- Market Revenue by Application - Vertical market analysis with growth projections across all major sectors
- Regional Revenue Analysis - Geographic market distribution, growth rates, and competitive positioning by region
- Comprehensive Company Profiles including AiM Future, Aistorm, Advanced Micro Devices (AMD), Alpha ICs, Amazon Web Services (AWS), Ambarella Inc., Anaflash, Andes Technology, Apple, Arm, Astrus Inc., Axelera AI, Axera Semiconductor, Baidu Inc., BirenTech, Black Sesame Technologies, Blaize, Blumind Inc., Brainchip Holdings Ltd., Cambricon, Ccvui (Xinsheng Intelligence), Celestial AI, Cerebras Systems, Ceremorphic, ChipIntelli, CIX Technology, CogniFiber, Corerain Technologies, DeGirum, Denglin Technology, DEEPX, d-Matrix, Eeasy Technology, EdgeCortix, Efinix, EnCharge AI, Enerzai, Enfabrica, Enflame, Esperanto Technologies, Etched.ai, Evomotion, Expedera, Flex Logix, Fractile, FuriosaAI, Gemesys, Google, Graphcore, GreenWaves Technologies, Groq, Gwanak Analog Co. Ltd., Hailo, Horizon Robotics, Houmo.ai, Huawei, HyperAccel, IBM, Iluvatar CoreX, Innatera Nanosystems, Intel, Intellifusion, Intelligent Hardware Korea (IHWK), Inuitive, Jeejio, Kalray SA, Kinara, KIST (Korea Institute of Science and Technology), Kneron, Krutrim, Kunlunxin Technology, Lightmatter, Lightstandard Technology, Lightelligence, Lumai, Luminous Computing, MatX, MediaTek, MemryX, Meta, Microsoft, Mobilint, Modular, Moffett AI, Moore Threads, Mythic, Nanjing SemiDrive Technology, Nano-Core Chip, National Chip, Neuchips, NeuronBasic, NeuReality, NeuroBlade, NextVPU, Nextchip Co. Ltd., NXP Semiconductors, Nvidia, Oculi, OpenAI, Panmnesia and more....
1 INTRODUCTION 16
- 1.1 What is an AI chip? 17
- 1.1.1 AI Acceleration 17
- 1.1.2 Hardware & Software Co-Design 18
- 1.1.3 Moore's Law 19
- 1.2 Key capabilities 19
- 1.3 History of AI Chip Development 20
- 1.4 Applications 21
- 1.5 AI Chip Architectures 22
- 1.6 Computing requirements 23
- 1.7 Semiconductor packaging 24
- 1.7.1 Evolution from 1D to 3D semiconductor packaging 25
- 1.8 AI chip market landscape 26
- 1.8.1 China 26
- 1.8.2 USA 28
- 1.8.2.1 The US CHIPS and Science Act of 2022 28
- 1.8.3 Europe 29
- 1.8.3.1 The European Chips Act of 2022 29
- 1.8.4 Rest of Asia 30
- 1.8.4.1 South Korea 30
- 1.8.4.2 Japan 31
- 1.8.4.3 Taiwan 31
- 1.9 Edge AI 31
- 1.9.1 Edge vs Cloud 32
- 1.9.2 Edge devices that utilize AI chips 33
- 1.9.3 Players in edge AI chips 33
- 1.9.4 Inference at the edge 35
- 1.10 Market drivers 35
- 1.11 Government funding and initiatives 36
- 1.12 Funding and investments 37
- 1.13 Market challenges 39
- 1.14 Market players 40
- 1.15 Future Outlook for AI Chips 41
- 1.15.1 Specialization 41
- 1.15.2 3D System Integration 42
- 1.15.3 Software Abstraction Layers 42
- 1.15.4 Edge-Cloud Convergence 42
- 1.15.5 Environmental Sustainability 42
- 1.15.6 Neuromorphic Photonics 42
- 1.15.7 New Materials 43
- 1.15.8 Efficiency Improvements 43
- 1.15.9 Automated Chip Generation 44
- 1.16 AI roadmap 45
- 1.17 Large AI Models 45
- 1.17.1 Scaling 45
- 1.17.2 Transformer architecture 46
- 1.17.3 Primary focus areas for AI research and development 47
- 1.17.4 AI performance improvements 48
- 1.17.5 Sustained growth of AI models 49
- 1.17.6 Energy consumption of AI model training 50
- 1.17.7 Hardware design inefficiencies in AI compute systems 50
- 1.17.8 Energy efficiency of ML systems 51
2 AI CHIP FABRICATION 53
- 2.1 Supply chain 53
- 2.2 Fab investments and capabilities 54
- 2.3 Manufacturing advances 55
- 2.3.1 Chiplets 56
- 2.3.2 3D Fabrication 56
- 2.3.3 Algorithm-Hardware Co-Design 56
- 2.3.4 Advanced Lithography 57
- 2.3.5 Novel Devices 57
- 2.4 Instruction Set Architectures 58
- 2.4.1 Instruction Set Architectures (ISAs) for AI workloads 58
- 2.4.2 CISC and RISC ISAs for AI accelerators 58
- 2.5 Programming Models and Execution Models 59
- 2.5.1 Programming model vs execution model 59
- 2.5.2 Von Neumann Architecture 60
- 2.6 Transistors 61
- 2.6.1 Transistor operation 62
- 2.6.2 Gate length reduction 63
- 2.6.3 Increasing Transistor Count 63
- 2.6.4 Planar FET to FinFET 64
- 2.6.5 GAAFET, MBCFET, RibbonFET 64
- 2.6.6 Complementary Field-Effect Transistors (CFETs) 65
- 2.6.7 Roadmaps 66
- 2.6.7.1 TSMC 66
- 2.6.7.2 Intel Foundry 66
- 2.6.7.3 Samsung Foundry 67
- 2.7 Advanced Semiconductor Packaging 71
- 2.7.1 1D to 3D semiconductor packaging 71
- 2.7.2 2.5D packaging 72
- 2.7.2.1 2.5D advanced semiconductor packaging technology 73
- 2.7.2.2 2.5D Advanced Semiconductor Packaging in AI Chips 73
- 2.7.2.3 Die Size Limitations 74
- 2.7.2.4 Integrated Heterogeneous Systems 75
- 2.7.2.5 Future System-in-Package Architecture 76
3 AI CHIP ARCHITECTURES 77
- 3.1 Distributed Parallel Processing 77
- 3.2 Optimized Data Flow 78
- 3.3 Flexible vs. Specialized Designs 78
- 3.4 Hardware for Training vs. Inference 79
- 3.5 Software Programmability 80
- 3.6 Architectural Optimization Goals 80
- 3.7 Innovations 81
- 3.7.1 Specialized Processing Units 81
- 3.7.2 Dataflow Optimization 81
- 3.7.3 Model Compression 82
- 3.7.4 Biologically-Inspired Designs 82
- 3.7.5 Analog Computing 83
- 3.7.6 Photonic Connectivity 83
- 3.8 Sustainability 84
- 3.8.1 Energy Efficiency 84
- 3.8.2 Green Data Centers 84
- 3.8.3 Eco-Electronics 84
- 3.8.4 Reusable Architectures & IP 85
- 3.8.5 Regulated Lifecycles 85
- 3.8.6 AI for Sustainability 85
- 3.8.7 AI Model Efficiency 86
- 3.9 Companies, by architecture 86
- 3.10 Hardware Architectures 87
- 3.10.1 ASICs, FPGAs, and GPUs used for neural network architectures 87
- 3.10.2 Types of AI Chips 87
- 3.10.3 TRL 88
- 3.10.4 Commercial AI chips 90
- 3.10.5 Emerging AI chips 90
- 3.10.6 General-purpose processors 92
4 TYPES OF AI CHIPS 93
- 4.1 Training Accelerators 93
- 4.2 Inference Accelerators 95
- 4.3 Automotive AI Chips 97
- 4.4 Smart Device AI Chips 99
- 4.5 Cloud Data Center Chips 101
- 4.6 Edge AI Chips 102
- 4.7 Neuromorphic Chips 103
- 4.8 FPGA-Based Solutions 104
- 4.9 Multi-Chip Modules 105
- 4.10 Emerging technologies 106
- 4.10.1 Novel Materials 107
- 4.10.1.1 2D materials 107
- 4.10.1.2 Photonic materials 107
- 4.10.1.3 Spintronic materials 108
- 4.10.1.4 Phase change materials 108
- 4.10.1.5 Neuromorphic materials 109
- 4.10.2 Advanced Packaging 109
- 4.10.3 Software Abstraction 110
- 4.10.4 Environmental Sustainability 110
- 4.10.1 Novel Materials 107
- 4.11 Specialized components 111
- 4.11.1 Sensor Interfacing 111
- 4.11.2 Memory Technologies 112
- 4.11.2.1 HBM stacks 112
- 4.11.2.2 GDDR 112
- 4.11.2.3 SRAM 112
- 4.11.2.4 STT-RAM 112
- 4.11.2.5 ReRAM 113
- 4.11.3 Software Frameworks 113
- 4.11.4 Data Center Design 113
- 4.12 AI-Capable Central Processing Units (CPUs) 114
- 4.12.1 Core architecture 115
- 4.12.2 CPU requirements 116
- 4.12.3 AI-capable CPUs 116
- 4.12.4 Intel Processors 117
- 4.12.5 AMD Processors 119
- 4.12.6 IBM Processors 119
- 4.12.7 Arm Processors 119
- 4.13 Graphics Processing Units (GPUs) 121
- 4.13.1 Types of AI GPUs 121
- 4.13.1.1 Data Center GPUs 124
- 4.13.1.2 NVIDIA 124
- 4.13.1.3 AMD 125
- 4.13.1.4 Intel 126
- 4.13.1.5 Chinese GPU manufacturers 126
- 4.13.1 Types of AI GPUs 121
- 4.14 Custom AI ASICs for Cloud Service Providers (CSPs) 126
- 4.14.1 Overview 126
- 4.14.2 Google TPU 129
- 4.14.3 Amazon 130
- 4.14.4 Microsoft 130
- 4.14.5 Meta 130
- 4.15 Other AI Chips 131
- 4.15.1 Heterogenous Matrix-Based AI Accelerators 132
- 4.15.1.1 Habana 132
- 4.15.1.2 Cambricon Technologies 133
- 4.15.1.3 Huawei 133
- 4.15.1.4 Baidu 133
- 4.15.1.5 Qualcomm 134
- 4.15.2 Spatial AI Accelerators 134
- 4.15.2.1 Cerebras 135
- 4.15.2.2 Graphcore 135
- 4.15.2.3 Groq 136
- 4.15.2.4 SambaNova 136
- 4.15.2.5 Untether AI 137
- 4.15.3 Coarse-Grained Reconfigurable Arrays (CGRAs) 137
- 4.15.1 Heterogenous Matrix-Based AI Accelerators 132
5 AI CHIP MARKETS 138
- 5.1 Market map 138
- 5.2 Data Centers 139
- 5.2.1 Market overview 139
- 5.2.2 Market players 139
- 5.2.3 Hardware 140
- 5.2.4 Trends 140
- 5.3 Automotive 141
- 5.3.1 Market overview 141
- 5.3.2 Market outlook 141
- 5.3.3 Autonomous Driving 142
- 5.3.3.1 Market players 142
- 5.3.4 Increasing power demands 143
- 5.3.5 Market players 143
- 5.4 Industry 4.0 144
- 5.4.1 Market overview 144
- 5.4.2 Applications 144
- 5.4.3 Market players 145
- 5.5 Smartphones 145
- 5.5.1 Market overview 145
- 5.5.2 Commercial examples 147
- 5.5.3 Smartphone chipset market 148
- 5.5.4 Process nodes 148
- 5.6 Tablets 149
- 5.6.1 Market overview 149
- 5.6.2 Market players 150
- 5.7 IoT & IIoT 150
- 5.7.1 Market overview 150
- 5.7.2 AI on the IoT edge 151
- 5.7.3 Consumer smart appliances 151
- 5.7.4 Market players 152
- 5.8 Computing 153
- 5.8.1 Market overview 153
- 5.8.2 Personal computers 153
- 5.8.3 Parallel computing 154
- 5.8.4 Low-precision computing 154
- 5.8.5 Market players 155
- 5.9 Drones & Robotics 155
- 5.9.1 Market overview 155
- 5.9.2 Market players 156
- 5.10 Wearables, AR glasses and hearables 156
- 5.10.1 Market overview 156
- 5.10.2 Applications 157
- 5.10.3 Market players 158
- 5.11 Sensors 159
- 5.11.1 Market overview 159
- 5.11.2 Challenges 159
- 5.11.3 Applications 159
- 5.11.4 Market players 160
- 5.12 Life Sciences 160
- 5.12.1 Market overview 160
- 5.12.2 Applications 161
- 5.12.3 Market players 162
6 GLOBAL MARKET REVENUES AND COSTS 162
- 6.1 Costs 162
- 6.2 Revenues by chip type, 2020-2036 164
- 6.3 Revenues by market, 2020-2036 165
- 6.4 Revenues by region, 2020-2036 167
7 COMPANY PROFILES 169 (142 company profiles)
8 APPENDIX 306
- 8.1 Research Methodology 306
9 REFERENCES 307
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. 24
- 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. 34
- Table 9. Market drivers for AI Chips. 35
- Table 10. AI chip government funding and initiatives. 36
- Table 11. AI chips funding and investment, by company. 36
- Table 12. Market challenges in AI chips. 39
- Table 13. Key players in AI chips. 40
- Table 14. System Type Comparison. 45
- Table 15. Comparison of RNNs/LSTMs vs Transformers. 46
- Table 16. Key Drivers 49
- Table 17. Power Ranges for Various AI Chip Types. 51
- Table 18. AI Chip Supply Chain. 53
- Table 19. Fab investments and capabilities. 54
- Table 20. Comparison of AI chip fabrication capabilities between IDMs (integrated device manufacturers) and dedicated foundries. 54
- Table 21. Programming model vs execution model. 59
- Table 22. Von Neumann compared with common programming models. 60
- Table 23. Key Metrics for Advanced Semiconductor Packaging Performance. 70
- Table 24. Goals driving the exploration into AI chip architectures. 79
- Table 25. Concepts from neuroscience influence architecture. 82
- Table 26. Companies by Architecture. 86
- Table 27. AI Chip Types. 87
- Table 28. Technology Readiness Level (TRL) Table for AI Chip Technologies. 88
- Table 29. Commercial AI Chips Advantages and Disadvantages. 89
- Table 30. Emerging AI Chips Advantages and Disadvantages. 91
- Table 31. Types of training accelerators for AI chips. 94
- Table 32. Types of inference accelerators for AI chips. 97
- Table 33. Types of Automotive AI chips. 98
- Table 34. Smart device AI chips. 100
- Table 35. Types of cloud data center AI chips. 101
- Table 36. Key types of edge AI chips. 102
- Table 37. Types of neuromorphic chips and their attributes. 104
- Table 38. Types of FPGA-based solutions for AI acceleration. 105
- Table 39. Types of multi-chip module (MCM) integration approaches for AI chips. 106
- Table 40. 2D materials in AI hardware. 107
- Table 41. Photonic materials for AI hardware. 107
- Table 42. Spintronic materials for AI hardware. 107
- Table 43. Phase change materials for AI hardware. 108
- Table 44. Neuromorphic materials in AI hardware. 109
- Table 45. Techniques for combining chiplets and dies using advanced packaging for AI chips. 109
- Table 46. Types of sensors. 111
- Table 47. Key CPU Requirements for HPC and AI Workloads. 115
- Table 48. AI GPU Types. 121
- Table 49. Data Center GPU Manufacturer Comparison. 122
- Table 50. CPU vs GPU Architecture Comparison. 123
- Table 51. AI ASICs. 127
- Table 52. Key AI chip products and solutions targeting automotive applications. 142
- Table 53. AI versus non-AI smartphones 145
- Table 54. Key chip fabrication process nodes used by various mobile AI chip designers. 148
- Table 55. AI versus non AI tablets. 149
- Table 56. Market players in AI chips for personal, parallel, and low-precision computing. 154
- Table 57. AI chip company products for drones and robotics. 156
- Table 58. Applications of AI chips in wearable devices. 157
- Table 59. Applications of ai chips and sensors and structural health monitoring. 159
- Table 60. Applications of AI chips in life sciences. 160
- Table 61. AI chip costs analysis-design, operation and fabrication. 162
- Table 62. Design, manufacturing, testing, and operational costs associated with leading-edge process nodes for AI chips. 162
- Table 63. Assembly, test, and packaging (ATP) costs associated with manufacturing AI chips. 162
- Table 64. Global market revenues by chip type, 2020-2036 (billions USD). 163
- Table 65. Global market revenues by market, 2020-2036 (billions USD). 165
- Table 66. Global market revenues by region, 2020-2036 (billions USD). 167
- Table 67. AMD AI chip range. 172
- Table 68. Applications of CV3-AD685 in autonomous driving. 177
- Table 69. Evolution of Apple Neural Engine. 180
List of Figures
- Figure 1. Nvidia H200 AI Chip. 16
- Figure 2. History of AI development. 20
- Figure 3. AI roadmap. 44
- Figure 4. Scaling Technology Roadmap. 68
- Figure 5. Device architecture roadmap. 69
- Figure 6. TRL of AI chip technologies. 88
- Figure 7. Nvidia A100 GPU . 93
- Figure 8. Google Cloud TPUs. 93
- Figure 9. Groq Node. 94
- Figure 10. Intel Movidius Myriad X. 95
- Figure 11. Qualcomm Cloud AI 100. 96
- Figure 12. Tesla FSD Chip. 97
- Figure 13. Qualcomm Snapdragon. 99
- Figure 14. Xeon CPUs for data center. 117
- Figure 15. Colossus™ MK2 IPU processor. 135
- Figure 16. AI chio market map. 138
- Figure 17. Global market revenues by chip type, 2020-2036 (billions USD). 164
- Figure 18. Global market revenues by market 2020-2036 (billions USD). 166
- Figure 19. Global market revenues by region, 2020-2036 (billions USD). 168
- Figure 20. AMD Radeon Instinct. 172
- Figure 21. AMD Ryzen 7040. 172
- Figure 22. Alveo V70. 173
- Figure 23. Versal Adaptive SOC. 173
- Figure 24. AMD’s MI300 chip. 173
- Figure 25. Cerebas WSE-2. 196
- Figure 26. DeepX NPU DX-GEN1. 204
- Figure 27. InferX X1. 216
- Figure 28. “Warboy”(AI Inference Chip). 218
- Figure 29. Google TPU. 220
- Figure 30. Colossus™ MK2 GC200 IPU. 222
- Figure 31. GreenWave’s GAP8 and GAP9 processors. 223
- Figure 32. Journey 5. 228
- Figure 33. IBM Telum processor. 232
- Figure 34. 11th Gen Intel® Core™ S-Series. 236
- Figure 35. Envise. 244
- Figure 36. Pentonic 2000. 249
- Figure 37. Meta Training and Inference Accelerator (MTIA). 251
- Figure 38. Azure Maia 100 and Cobalt 100 chips. 252
- Figure 39. Mythic MP10304 Quad-AMP PCIe Card. 257
- Figure 40. Nvidia H200 AI chip. 267
- Figure 41. Grace Hopper Superchip. 268
- Figure 42. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below). 271
- Figure 43. Cloud AI 100. 273
- Figure 44. Peta Op chip. 276
- Figure 45. Cardinal SN10 RDU. 278
- Figure 46. MLSoC™. 285
- Figure 47. Grayskull. 292
- Figure 48. Tesla D1 chip. 293
The report includes these components:
- PDF report download/by email. Print edition also available.
- Comprehensive Excel spreadsheet of all data.
- Mid-year Update
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