세계의 AI 칩 시장(2026-2036년)

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 Euro-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 Euro-15 million for brain-inspired processors using spiking neural networks, while Semron secured Euro-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....

TABLE OF CONTENTS

1. INTRODUCTION

• 1.1. What is an AI chip?
  • 1.1.1. AI Acceleration
  • 1.1.2. Hardware & Software Co-Design
  • 1.1.3. Moore's Law
• 1.2. Key capabilities
• 1.3. History of AI Chip Development
• 1.4. Applications
• 1.5. AI Chip Architectures
• 1.6. Computing requirements
• 1.7. Semiconductor packaging
  • 1.7.1. Evolution from 1D to 3D semiconductor packaging
• 1.8. AI chip market landscape
  • 1.8.1. China
  • 1.8.2. USA
    • 1.8.2.1. The US CHIPS and Science Act of 2022
  • 1.8.3. Europe
    • 1.8.3.1. The European Chips Act of 2022
  • 1.8.4. Rest of Asia
    • 1.8.4.1. South Korea
    • 1.8.4.2. Japan
    • 1.8.4.3. Taiwan
• 1.9. Edge AI
  • 1.9.1. Edge vs Cloud
  • 1.9.2. Edge devices that utilize AI chips
  • 1.9.3. Players in edge AI chips
  • 1.9.4. Inference at the edge
• 1.10. Market drivers
• 1.11. Government funding and initiatives
• 1.12. Funding and investments
• 1.13. Market challenges
• 1.14. Market players
• 1.15. Future Outlook for AI Chips
  • 1.15.1. Specialization
  • 1.15.2. 3D System Integration
  • 1.15.3. Software Abstraction Layers
  • 1.15.4. Edge-Cloud Convergence
  • 1.15.5. Environmental Sustainability
  • 1.15.6. Neuromorphic Photonics
  • 1.15.7. New Materials
  • 1.15.8. Efficiency Improvements
  • 1.15.9. Automated Chip Generation
• 1.16. AI roadmap
• 1.17. Large AI Models
  • 1.17.1. Scaling
  • 1.17.2. Transformer architecture
  • 1.17.3. Primary focus areas for AI research and development
  • 1.17.4. AI performance improvements
  • 1.17.5. Sustained growth of AI models
  • 1.17.6. Energy consumption of AI model training
  • 1.17.7. Hardware design inefficiencies in AI compute systems
  • 1.17.8. Energy efficiency of ML systems

2. AI CHIP FABRICATION

• 2.1. Supply chain
• 2.2. Fab investments and capabilities
• 2.3. Manufacturing advances
  • 2.3.1. Chiplets
  • 2.3.2. 3D Fabrication
  • 2.3.3. Algorithm-Hardware Co-Design
  • 2.3.4. Advanced Lithography
  • 2.3.5. Novel Devices
• 2.4. Instruction Set Architectures
  • 2.4.1. Instruction Set Architectures (ISAs) for AI workloads
  • 2.4.2. CISC and RISC ISAs for AI accelerators
• 2.5. Programming Models and Execution Models
  • 2.5.1. Programming model vs execution model
  • 2.5.2. Von Neumann Architecture
• 2.6. Transistors
  • 2.6.1. Transistor operation
  • 2.6.2. Gate length reduction
  • 2.6.3. Increasing Transistor Count
  • 2.6.4. Planar FET to FinFET
  • 2.6.5. GAAFET, MBCFET, RibbonFET
  • 2.6.6. Complementary Field-Effect Transistors (CFETs)
  • 2.6.7. Roadmaps
    • 2.6.7.1. TSMC
    • 2.6.7.2. Intel Foundry
    • 2.6.7.3. Samsung Foundry
• 2.7. Advanced Semiconductor Packaging
  • 2.7.1. 1D to 3D semiconductor packaging
  • 2.7.2. 2.5D packaging
    • 2.7.2.1. 2.5D advanced semiconductor packaging technology
    • 2.7.2.2. 2.5D Advanced Semiconductor Packaging in AI Chips
    • 2.7.2.3. Die Size Limitations
    • 2.7.2.4. Integrated Heterogeneous Systems
    • 2.7.2.5. Future System-in-Package Architecture

3. AI CHIP ARCHITECTURES

• 3.1. Distributed Parallel Processing
• 3.2. Optimized Data Flow
• 3.3. Flexible vs. Specialized Designs
• 3.4. Hardware for Training vs. Inference
• 3.5. Software Programmability
• 3.6. Architectural Optimization Goals
• 3.7. Innovations
  • 3.7.1. Specialized Processing Units
  • 3.7.2. Dataflow Optimization
  • 3.7.3. Model Compression
  • 3.7.4. Biologically-Inspired Designs
  • 3.7.5. Analog Computing
  • 3.7.6. Photonic Connectivity
• 3.8. Sustainability
  • 3.8.1. Energy Efficiency
  • 3.8.2. Green Data Centers
  • 3.8.3. Eco-Electronics
  • 3.8.4. Reusable Architectures & IP
  • 3.8.5. Regulated Lifecycles
  • 3.8.6. AI for Sustainability
  • 3.8.7. AI Model Efficiency
• 3.9. Companies, by architecture
• 3.10. Hardware Architectures
  • 3.10.1. ASICs, FPGAs, and GPUs used for neural network architectures
  • 3.10.2. Types of AI Chips
  • 3.10.3. TRL
  • 3.10.4. Commercial AI chips
  • 3.10.5. Emerging AI chips
  • 3.10.6. General-purpose processors

4. TYPES OF AI CHIPS

• 4.1. Training Accelerators
• 4.2. Inference Accelerators
• 4.3. Automotive AI Chips
• 4.4. Smart Device AI Chips
• 4.5. Cloud Data Center Chips
• 4.6. Edge AI Chips
• 4.7. Neuromorphic Chips
• 4.8. FPGA-Based Solutions
• 4.9. Multi-Chip Modules
• 4.10. Emerging technologies
  • 4.10.1. Novel Materials
    • 4.10.1.1. 2D materials
    • 4.10.1.2. Photonic materials
    • 4.10.1.3. Spintronic materials
    • 4.10.1.4. Phase change materials
    • 4.10.1.5. Neuromorphic materials
  • 4.10.2. Advanced Packaging
  • 4.10.3. Software Abstraction
  • 4.10.4. Environmental Sustainability
• 4.11. Specialized components
  • 4.11.1. Sensor Interfacing
  • 4.11.2. Memory Technologies
    • 4.11.2.1. HBM stacks
    • 4.11.2.2. GDDR
    • 4.11.2.3. SRAM
    • 4.11.2.4. STT-RAM
    • 4.11.2.5. ReRAM
  • 4.11.3. Software Frameworks
  • 4.11.4. Data Center Design
• 4.12. AI-Capable Central Processing Units (CPUs)
  • 4.12.1. Core architecture
  • 4.12.2. CPU requirements
  • 4.12.3. AI-capable CPUs
  • 4.12.4. Intel Processors
  • 4.12.5. AMD Processors
  • 4.12.6. IBM Processors
  • 4.12.7. Arm Processors
• 4.13. Graphics Processing Units (GPUs)
  • 4.13.1. Types of AI GPUs
    • 4.13.1.1. Data Center GPUs
    • 4.13.1.2. NVIDIA
    • 4.13.1.3. AMD
    • 4.13.1.4. Intel
    • 4.13.1.5. Chinese GPU manufacturers
• 4.14. Custom AI ASICs for Cloud Service Providers (CSPs)
  • 4.14.1. Overview
  • 4.14.2. Google TPU
  • 4.14.3. Amazon
  • 4.14.4. Microsoft
  • 4.14.5. Meta
• 4.15. Other AI Chips
  • 4.15.1. Heterogenous Matrix-Based AI Accelerators
    • 4.15.1.1. Habana
    • 4.15.1.2. Cambricon Technologies
    • 4.15.1.3. Huawei
    • 4.15.1.4. Baidu
    • 4.15.1.5. Qualcomm
  • 4.15.2. Spatial AI Accelerators
    • 4.15.2.1. Cerebras
    • 4.15.2.2. Graphcore
    • 4.15.2.3. Groq
    • 4.15.2.4. SambaNova
    • 4.15.2.5. Untether AI
  • 4.15.3. Coarse-Grained Reconfigurable Arrays (CGRAs)

5. AI CHIP MARKETS

• 5.1. Market map
• 5.2. Data Centers
  • 5.2.1. Market overview
  • 5.2.2. Market players
  • 5.2.3. Hardware
  • 5.2.4. Trends
• 5.3. Automotive
  • 5.3.1. Market overview
  • 5.3.2. Market outlook
  • 5.3.3. Autonomous Driving
    • 5.3.3.1. Market players
  • 5.3.4. Increasing power demands
  • 5.3.5. Market players
• 5.4. Industry 4.0
  • 5.4.1. Market overview
  • 5.4.2. Applications
  • 5.4.3. Market players
• 5.5. Smartphones
  • 5.5.1. Market overview
  • 5.5.2. Commercial examples
  • 5.5.3. Smartphone chipset market
  • 5.5.4. Process nodes
• 5.6. Tablets
  • 5.6.1. Market overview
  • 5.6.2. Market players
• 5.7. IoT & IIoT
  • 5.7.1. Market overview
  • 5.7.2. AI on the IoT edge
  • 5.7.3. Consumer smart appliances
  • 5.7.4. Market players
• 5.8. Computing
  • 5.8.1. Market overview
  • 5.8.2. Personal computers
  • 5.8.3. Parallel computing
  • 5.8.4. Low-precision computing
  • 5.8.5. Market players
• 5.9. Drones & Robotics
  • 5.9.1. Market overview
  • 5.9.2. Market players
• 5.10. Wearables, AR glasses and hearables
  • 5.10.1. Market overview
  • 5.10.2. Applications
  • 5.10.3. Market players
• 5.11. Sensors
  • 5.11.1. Market overview
  • 5.11.2. Challenges
  • 5.11.3. Applications
  • 5.11.4. Market players
• 5.12. Life Sciences
  • 5.12.1. Market overview
  • 5.12.2. Applications
  • 5.12.3. Market players

6. GLOBAL MARKET REVENUES AND COSTS

• 6.1. Costs
• 6.2. Revenues by chip type, 2020-2036
• 6.3. Revenues by market, 2020-2036
• 6.4. Revenues by region, 2020-2036

7. COMPANY PROFILES(142 company profiles)

8. APPENDIX

• 8.1. Research Methodology

9. REFERENCES