查看购物车
  • Traditional Chinese
  • English
  • Japanese
AI晶片的全球市场(2026年～2036年)
市场调查报告书
商品编码
1812613

AI晶片的全球市场(2026年～2036年)

The Global Artificial Intelligence (AI) Chips Market 2026-2036
出版日期: | 出版商: Future Markets, Inc. | 英文 311 Pages, 69 Tables, 48 Figures | 订单完成后即时交付

价格

2025年，全球人工智慧晶片市场正经历前所未有的成长。 2025年第一季度，75家新创公司共筹集超过20亿美元的资金，彰显了市场的强劲成长。人工智慧晶片及其赋能技术成为主要赢家，其中，开发用于晶片和资料中心基础设施的光通讯技术的公司筹集了超过4亿美元的资金。值得注意的是，光是第一季度，就有六家公司筹集了至少1亿美元的投资。 2024年至2025年期间的几轮融资表明，投资者对各种人工智慧晶片技术的持续信心。值得关注的欧洲投资包括高效能人工智慧推理晶片开发商VSORA，该公司在Otium的领投下筹集了4,600万美元；以及Axelera AI，该公司获得了欧洲高效能运算联合委员会（EuroHPC Joint Undertaking）的6,160万欧元资助，用于其基于RISC-V的加速人工智慧平台。亚洲市场发展势头强劲，Rebellions 在由韩国电信公司 (KT Corp) 领投的 B 轮融资中获得了 1.24 亿美元，用于其特定领域 AI 处理器；HyperAccel 则为其生成式 AI 推理解决方案筹集了 4000 万美元。

新技术吸引了大量资本，尤其是在神经形态计算和模拟处理领域。 Innatera Nanosystems 为其使用脉衝神经网路的类脑处理器筹集了 1500 万欧元，Semron 为其使用记忆电容器的模拟记忆体计算筹集了 730 万欧元。这些投资显示了业界对超低功耗边缘 AI 解决方案的追求。

Celestial AI 在富达管理研究公司 (Fidelity Management & Research Company) 领投的 C1 轮融资中获得了 2.5 亿美元，用于其光子织物技术。同样，量子运算平台也吸引了大量投资，中性原子量子电脑 QuEra Computing 从Google和软银愿景基金筹集了 2.3 亿美元。日本国家能源技术开发机构 (NEDO) 提供了大量拨款，其中包括 4,670 万美元的政府资金，用于 EdgeCortix 的 AI 晶片开发。欧洲的 AI 发展势头强劲，支持 NeuReality 和 CogniFiber 等公司的欧洲创新委员会基金参与了多轮融资。

北美公司保持着强劲的融资活动，Etched 为其 Transformer 专用 ASIC 筹集了 1.2 亿美元，Groq 为其语言处理单元完成了 6.4 亿美元的 D 轮融资。 Tenstorrent 由三星证券领投的 6.93 亿美元 D 轮融资表明了市场对基于 RISC-V 的 AI 处理器 IP 的持续信心。持续的投资流入反映了 AI 运算需求的根本变化。产业分析师预测，2025 年后，纯 AI 推理市场的成长速度将超过训练市场，从而推动对专用推理加速器的需求。 Recogni、SiMa.ai 和 Blaize 等公司已经筹集了大量资金，专注于推理优化解决方案。

边缘运算是一个关键的成长点，吸引了众多致力于开发超低功耗解决方案的公司的大量投资。 Blumind 为模拟 AI 推理晶片融资 1,410 万美元，Mobilint 为边缘 NPU 晶片融资 1,530 万美元，显示投资者对边缘 AI 机会的认可。

竞争格局持续演变，新的架构方法日益受到青睐。 Fractile 为记忆体处理晶片融资 1,500 万美元，Vaire Computing 为绝热可逆计算融资 450 万美元，展示了应对 AI 能耗课题的新方法。

本报告探讨并分析了全球 AI 晶片市场，提供了市场动态、技术创新、竞争格局以及多个应用领域的未来成长机会等资讯。

目录

第1章 简介

  • 什么是 AI 晶片？
  • 关键能力
  • AI 晶片发展历史
  • 应用
  • AI 晶片架构
  • 计算要求
  • 半导体封装
  • AI 晶片市场格局
  • 边缘 AI
  • 市场推动因素
  • 政府资金和计划
  • 资金和投资
  • 市场课题
  • 市场参与者
  • AI 晶片未来展望
  • AI 路线图
  • 大规模 AI 模型

第2章 AI晶片的製造

  • 供应链
  • 晶圆厂投资和生产能力
  • 製造的进步
  • 指令集架构
  • 编程模式和实行模式
  • 电晶体
  • 先进半导体包装

第3章 AI晶片结构

  • 分散式平行处理
  • 优化资料流
  • 灵活设计 vs. 专业设计
  • 训练硬体 vs. 推理硬件
  • 软体可程式性
  • 架构最佳化目标
  • 创新
  • 永续性
  • 依架构分类的公司
  • 硬体架构

第4章 AI晶片的类型

  • 训练加速器
  • 推理加速器
  • 汽车 AI 晶片
  • 智慧型装置 AI 晶片
  • 云端资料中心晶片
  • 边缘 AI 晶片
  • 神经形态晶片
  • 基于 FPGA 的解决方案
  • 多晶片模组
  • 新技术
  • 专用元件
  • 支援 AI CPU
  • GPU
  • 针对云端服务供应商 (CSP) 的客製化 AI ASIC
  • 其他 AI 晶片

第5章 AI晶片市场

  • 市场地图
  • 资料中心
  • 汽车
  • 工业4.0
  • 智慧型手机
  • 平板电脑
  • IoT·IIoT
  • 运算
  • 无人机·机器人工学
  • 穿戴式·AR眼镜·听戴式装置
  • 感测器
  • 生命科学

第6章 世界市场收益和成本

  • 成本
  • 收益:各晶片类型(2020年～2036年)
  • 收益:各市场(2020年～2036年)
  • 收益:各地区(2020年～2036年)

第7章 企业简介(企业142公司的简介)

第8章 附录

第9章 参考文献

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

List of Tables

  • Table 1. Markets and applications for AI chips
  • Table 2. AI Chip Architectures
  • Table 3. Computing requirements and constraints
  • Table 4. Computing requirements and constraints by applications
  • Table 5. Advantages and disadvantages of edge AI
  • Table 6. Edge vs Cloud
  • Table 7. Edge devices that utilize AI chips
  • Table 8. Players in edge AI chips
  • Table 9. Market drivers for AI Chips
  • Table 10. AI chip government funding and initiatives
  • Table 11. AI chips funding and investment, by company
  • Table 12. Market challenges in AI chips
  • Table 13. Key players in AI chips
  • Table 14. System Type Comparison
  • Table 15. Comparison of RNNs/LSTMs vs Transformers
  • Table 16. Key Drivers
  • Table 17. Power Ranges for Various AI Chip Types
  • Table 18. AI Chip Supply Chain
  • Table 19. Fab investments and capabilities
  • Table 20. Comparison of AI chip fabrication capabilities between IDMs (integrated device manufacturers) and dedicated foundries
  • Table 21. Programming model vs execution model
  • Table 22. Von Neumann compared with common programming models
  • Table 23. Key Metrics for Advanced Semiconductor Packaging Performance
  • Table 24. Goals driving the exploration into AI chip architectures
  • Table 25. Concepts from neuroscience influence architecture
  • Table 26. Companies by Architecture
  • Table 27. AI Chip Types
  • Table 28. Technology Readiness Level (TRL) Table for AI Chip Technologies
  • Table 29. Commercial AI Chips Advantages and Disadvantages
  • Table 30. Emerging AI Chips Advantages and Disadvantages
  • Table 31. Types of training accelerators for AI chips
  • Table 32. Types of inference accelerators for AI chips
  • Table 33. Types of Automotive AI chips
  • Table 34. Smart device AI chips
  • Table 35. Types of cloud data center AI chips
  • Table 36. Key types of edge AI chips
  • Table 37. Types of neuromorphic chips and their attributes
  • Table 38. Types of FPGA-based solutions for AI acceleration
  • Table 39. Types of multi-chip module (MCM) integration approaches for AI chips
  • Table 40. 2D materials in AI hardware
  • Table 41. Photonic materials for AI hardware
  • Table 42. Spintronic materials for AI hardware
  • Table 43. Phase change materials for AI hardware
  • Table 44. Neuromorphic materials in AI hardware
  • Table 45. Techniques for combining chiplets and dies using advanced packaging for AI chips
  • Table 46. Types of sensors
  • Table 47. Key CPU Requirements for HPC and AI Workloads
  • Table 48. AI GPU Types
  • Table 49. Data Center GPU Manufacturer Comparison
  • Table 50. CPU vs GPU Architecture Comparison
  • Table 51. AI ASICs
  • Table 52. Key AI chip products and solutions targeting automotive applications
  • Table 53. AI versus non-AI smartphones
  • Table 54. Key chip fabrication process nodes used by various mobile AI chip designers
  • Table 55. AI versus non AI tablets
  • Table 56. Market players in AI chips for personal, parallel, and low-precision computing
  • Table 57. AI chip company products for drones and robotics
  • Table 58. Applications of AI chips in wearable devices
  • Table 59. Applications of ai chips and sensors and structural health monitoring
  • Table 60. Applications of AI chips in life sciences
  • Table 61. AI chip costs analysis-design, operation and fabrication
  • Table 62. Design, manufacturing, testing, and operational costs associated with leading-edge process nodes for AI chips
  • Table 63. Assembly, test, and packaging (ATP) costs associated with manufacturing AI chips
  • Table 64. Global market revenues by chip type, 2020-2036 (billions USD)
  • Table 65. Global market revenues by market, 2020-2036 (billions USD)
  • Table 66. Global market revenues by region, 2020-2036 (billions USD)
  • Table 67. AMD AI chip range
  • Table 68. Applications of CV3-AD685 in autonomous driving
  • Table 69. Evolution of Apple Neural Engine

List of Figures

  • Figure 1. Nvidia H200 AI Chip
  • Figure 2. History of AI development
  • Figure 3. AI roadmap
  • Figure 4. Scaling Technology Roadmap
  • Figure 5. Device architecture roadmap
  • Figure 6. TRL of AI chip technologies
  • Figure 7. Nvidia A100 GPU
  • Figure 8. Google Cloud TPUs
  • Figure 9. Groq Node
  • Figure 10. Intel Movidius Myriad X
  • Figure 11. Qualcomm Cloud AI 100
  • Figure 12. Tesla FSD Chip
  • Figure 13. Qualcomm Snapdragon
  • Figure 14. Xeon CPUs for data center
  • Figure 15. Colossus(TM) MK2 IPU processor
  • Figure 16. AI chio market map
  • Figure 17. Global market revenues by chip type, 2020-2036 (billions USD)
  • Figure 18. Global market revenues by market 2020-2036 (billions USD)
  • Figure 19. Global market revenues by region, 2020-2036 (billions USD)
  • Figure 20. AMD Radeon Instinct
  • Figure 21. AMD Ryzen 7040
  • Figure 22. Alveo V70
  • Figure 23. Versal Adaptive SOC
  • Figure 24. AMD's MI300 chip
  • Figure 25. Cerebas WSE-2
  • Figure 26. DeepX NPU DX-GEN1
  • Figure 27. InferX X1
  • Figure 28. "Warboy"(AI Inference Chip)
  • Figure 29. Google TPU
  • Figure 30. Colossus(TM) MK2 GC200 IPU
  • Figure 31. GreenWave's GAP8 and GAP9 processors
  • Figure 32. Journey 5
  • Figure 33. IBM Telum processor
  • Figure 34. 11th Gen Intel-R Core(TM) S-Series
  • Figure 35. Envise
  • Figure 36. Pentonic 2000
  • Figure 37. Meta Training and Inference Accelerator (MTIA)
  • Figure 38. Azure Maia 100 and Cobalt 100 chips
  • Figure 39. Mythic MP10304 Quad-AMP PCIe Card
  • Figure 40. Nvidia H200 AI chip
  • Figure 41. Grace Hopper Superchip
  • Figure 42. Panmnesia memory expander module (top) and chassis loaded with switch and expander modules (below)
  • Figure 43. Cloud AI 100
  • Figure 44. Peta Op chip
  • Figure 45. Cardinal SN10 RDU
  • Figure 46. MLSoC(TM)
  • Figure 47. Grayskull
  • Figure 48. Tesla D1 chip