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市场调查报告书
商品编码
1952836

边缘人工智慧晶片：技术、市场及预测（2026-2036）

Edge AI Chips: Technologies, Markets, and Forecasts 2026-2036
出版日期: | 出版商: Future Markets, Inc. | 英文 126 Pages, 34 Tables, 25 Figures | 订单完成后即时交付

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随着人工智慧从集中式云端资料中心迁移到资料生成设备（例如智慧型手机、汽车、机器人、工业感测器和个人电脑），全球边缘人工智慧晶片市场正经历前所未有的成长。边缘人工智慧晶片，包括神经网路处理单元 (NPU)、图形处理器 (GPU) 和中央处理器 (CPU)，使设备能够在本地做出智慧决策，而无需依赖云端连接。这消除了延迟，增强了资料隐私，降低了频宽需求，并支援在安全关键型应用中实现即时自主运行。预计到 2036 年，边缘人工智慧晶片市场规模将超过 800 亿美元，主要驱动力来自五大关键应用领域：汽车、人工智慧智慧型手机、人工智慧个人电脑、人形机器人和用于预测性维护的人工智慧感测器。

汽车产业是最大的成长机会之一，随着 SAE L2+ 级到 L3 级自动驾驶技术的进步，法律责任从驾驶员转移到汽车製造商 (OEM)，因此对边缘人工智慧运算能力的需求显着增强。 智慧座舱系统是汽车产业的另一个细分市场，需要专门的人工智慧处理能力来实现语音助理、驾驶监控、手势辨识和扩增实境显示等功能。汽车产业与消费性电子产业一样，将自动驾驶和智慧座舱功能结合，成为两大边缘人工智慧晶片市场之一。

人工智慧智慧型手机在边缘人工智慧晶片市场中占主导地位，截至2026年1月，所有主要OEM厂商都已在其旗舰设备中整合人工智慧功能。人工智慧PC的专用人工智慧处理能力超过40 TOPS，在2025年仅占新PC销量的不到10%，但预计到2030年代初将占新PC销量的大部分。英特尔、高通、苹果和AMD的平台预计将争夺市场占有率。

人形机器人被定位为一个发展中但极具前景的应用领域。截至2026年，它们在汽车製造领域的应用正在不断扩大，预计未来十年将扩展到安防、监控和家庭环境领域。 随着超出目前拣货和物流作业的复杂任务数量不断增加，每个机器人所需的 AI 运算能力预计将显着提升。

本报告分析了全球边缘 AI 晶片市场，提供了 54 家公司的详细概况，包括成熟的半导体公司、专注于 AI 的新创公司和云端供应商的边缘解决方案，以及技术架构、应用市场、竞争格局和地理预测。

目录

第一章：摘要整理

  • 市场概览
    • 市场规模
    • 地理市场
    • 技术架构演进时间轴
  • 人工智慧方法论及终端市场应用简介
    • 边缘部署的机器学习基础
    • 终端市场应用概览
  • 关键方面
  • 地理预测分析
    • 美国
    • 中国
    • 欧洲
    • 世界其他地区

第二章：边缘人工智慧技术架构

  • NPU实现
  • SoC整合策略
  • 能源效率与效能最佳化
    • 低于 7W 的散热要求
    • TOPS/W 最佳化技术
    • 模型压缩与量化
  • 模拟计算与记忆体处理
  • 专用 NPU 架构
  • 基于 GPU 的边缘解决方案与专用 DPU 的比较
  • 边缘 AI 晶片供应链分析
    • CPU 供应链
    • NPU 供应链
    • GPU 供应链
    • 代工与製造供应链
  • 尖端半导体製造流程概述
    • 当前尖端製程（3nm、4nm）
    • 下一代製程（2nm）
    • 先进封装技术
    • 製程技术对边缘 AI 晶片成本的影响

第三章 应用市场分析

  • 工业物联网与製造应用
    • 预测性维护系统
    • 品质控制与检测
    • 即时分析与最佳化
  • 智慧型手机和行动装置整合
    • AI赋能的CPU整合
    • 专用AI加速器实现
    • 连续处理功能
    • AI PC市场
    • AI智慧型手机市场：主要功能与旗舰手机基准测试
  • 汽车与交通运输系统
    • SAE自动驾驶等级和边缘AI要求
    • 自动驾驶边缘AI处理器
    • 智慧座舱系统
  • 人形机器人应用
    • 目前部署状态和应用
    • 人形机器人边缘AI处理要求
    • 面向人形机器人的边缘AI晶片公司机器人
  • 智慧城市与基础建设应用
  • 医疗保健与穿戴式装置整合
  • 消费性电子与智慧家庭
  • 竞争格局与市场参与者
    • 现有主要半导体公司
    • 专注于人工智慧的新创公司
    • 云端提供者边缘解决方案
  • 市场驱动因素与技术趋势
    • 延迟要求与即时处理需求
    • 资料隐私与安全需求分析
    • 解决频宽限制与连线挑战
    • 评估物联网设备激增的影响
    • 边缘云端运算架构的演进
    • 优化电源效率与电池寿命
    • 自主系统处理需求
    • 人形机器人处理需求
    • 中美半导体市场动态与出口限制

第四章 公司简介（54 家公司简介）

第五章 参考资料

The global market for edge AI chips is entering a period of unprecedented growth as artificial intelligence transitions from centralised cloud data centers to the devices where data is generated - smartphones, vehicles, robots, industrial sensors, and personal computers. Edge AI chips, encompassing Neural Processing Units (NPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) optimised for machine learning inference, enable devices to make intelligent decisions locally, without reliance on cloud connectivity. This eliminates latency, enhances data privacy, reduces bandwidth requirements, and enables real-time autonomous operation in safety-critical applications. The edge AI chip market is forecast to exceed US$80 billion by 2036, driven by five key application segments: automotive, AI smartphones, AI PCs, humanoid robots, and AI sensors for predictive maintenance.

This report provides a comprehensive analysis of the edge AI chip market, covering technology architectures, application markets, competitive dynamics, geographic forecasts, and 54 detailed company profiles spanning established semiconductor giants, AI-focused startups, and cloud provider edge solutions. Market forecasts are provided from 2026 to 2036, segmented by geographic region (United States, China, Europe, and Rest of World) and by application. The report delivers actionable intelligence for semiconductor companies, chip designers, OEMs, system integrators, investors, and policymakers navigating this rapidly evolving market.

The automotive sector represents one of the highest-growth opportunities, with the transition from SAE Level 2+ to Level 3 autonomous driving shifting legal responsibility from the driver to the OEM, necessitating substantially greater edge AI compute. Intelligent cockpit systems represent an additional automotive sub-market requiring dedicated AI processing for voice assistants, driver monitoring, gesture recognition, and augmented reality displays. Together, autonomous driving and intelligent cockpit functions make automotive one of the two largest edge AI chip markets alongside consumer electronics.

AI smartphones dominate the edge AI chip market by volume, with every major OEM now offering AI-enabled features on flagship devices as of January 2026. The report benchmarks flagship AI processors from Apple, Qualcomm, MediaTek, Samsung, Google, and Huawei, and analyses the premiumization trend that is driving mid-range phones to eat into budget phone market share. AI PCs, defined as those exceeding 40 TOPS of dedicated AI processing, represented less than 10% of new PC sales in 2025 but are expected to constitute the majority of new sales by the early 2030s, with platforms from Intel, Qualcomm, Apple, and AMD competing for market share.

Humanoid robots are identified as a nascent but high-potential application segment. As of 2026, deployments are scaling on automotive manufacturing floors, with expansion into patrolling, surveillance, and household environments expected over the next decade. The required AI compute per robot is forecast to increase significantly as tasks grow in complexity beyond current picking and logistics operations.

The report examines the edge AI chip supply chain across CPU, NPU, and GPU architectures, including a detailed review of cutting-edge semiconductor manufacturing processes at 3nm, 2nm, and beyond, covering TSMC, Samsung Foundry, and Intel. Advanced packaging technologies including chiplets, 2.5D/3D integration, and fan-out wafer-level packaging are analysed for their impact on edge AI processor capability and cost. The geopolitical dimension is covered extensively, including the impact of US export controls on the China market, domestic Chinese semiconductor self-sufficiency efforts, and government investment programmes including the CHIPS and Science Act, the European Chips Act, and equivalent programmes in Japan and South Korea.

Report Contents

  • Executive summary with market size data and geographic market analysis
  • Introduction to AI methods and machine learning fundamentals for edge deployment
  • Geographic market forecasts 2026-2036 segmented by US, China, Europe, and Rest of World
  • Edge AI technology architecture analysis: NPU, GPU, CPU, SoC integration, analog computing, in-memory processing
  • Edge AI chip supply chain analysis covering CPU, NPU, and GPU value chains
  • Cutting-edge semiconductor manufacturing processes review: 3nm, 2nm, GAA, FinFET, advanced packaging
  • Predictive maintenance systems with case studies and edge AI sensor market analysis
  • AI smartphone market analysis with key features and flagship phone processor benchmarking
  • AI PC market analysis: definition, cutting-edge technologies, product benchmarking
  • Automotive edge AI: SAE levels of autonomy framework, autonomous driving processors, intelligent cockpit systems with case studies
  • Humanoid robot applications: deployment status, edge AI processing requirements, market projections, case studies
  • Smart cities and infrastructure applications
  • Healthcare and wearable device integration
  • Consumer electronics and home automation
  • Competitive landscape and market player analysis
  • Market drivers and technology trends including US-China semiconductor dynamics and export controls
  • 54 company profiles with product portfolios, technology architectures, funding, partnerships, and strategic positioning

Companies Profiled include Advanced Micro Devices (AMD), Alpha ICs, Amazon Web Services (AWS), Ambarella, Anaflash, Apple, Axelera AI, Axera Semiconductor, Blaize, BrainChip Holdings, Cerebras Systems, Corerain Technologies, DEEPX, DeGirum, EdgeCortix, Efinix, EnCharge AI, ENERZAi, Google, Graphcore, GreenWaves Technologies, Gwanak Analog, Hailo, Huawei, Innatera Nanosystems and more......

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY

  • 1.1 Market overview
    • 1.1.1 Market Size
    • 1.1.2 Geographic Market
    • 1.1.3 Technology Architecture Evolution Timeline
  • 1.2 Introduction to AI Methods and End Market Applications
    • 1.2.1 Machine Learning Fundamentals for Edge Deployment
    • 1.2.2 End Market Applications Overview
  • 1.3 Key Aspects
  • 1.4 Geographic Forecast Analysis
    • 1.4.1 United States
    • 1.4.2 China
    • 1.4.3 Europe
    • 1.4.4 Rest of World

2 EDGE AI TECHNOLOGY ARCHITECTURES

  • 2.1 Neural Processing Unit (NPU) Implementations
  • 2.2 System-on-Chip (SoC) Integration Strategies
  • 2.3 Power Efficiency and Performance Optimization
    • 2.3.1 Sub-7W Thermal Envelope Requirements
    • 2.3.2 TOPS/W Optimization Methodologies
    • 2.3.3 Model Compression and Quantization
  • 2.4 Analog Computing and In-Memory Processing
  • 2.5 Dedicated Neural Processing Unit Architectures
  • 2.6 GPU-Based Edge Solutions vs. Specialized DPUs
  • 2.7 Edge AI Chip Supply Chain Analysis
    • 2.7.1 CPU Supply Chain
    • 2.7.2 NPU Supply Chain
    • 2.7.3 GPU Supply Chain
    • 2.7.4 Foundry and Manufacturing Supply Chain
  • 2.8 Cutting-Edge Semiconductor Manufacturing Processes Review
    • 2.8.1 Current Leading-Edge Processes (3nm and 4nm)
    • 2.8.2 Next-Generation Processes (2nm)
    • 2.8.3 Advanced Packaging Technologies
    • 2.8.4 Impact of Process Technology on Edge AI Chip Cost

3 APPLICATION MARKET ANALYSIS

  • 3.1 Industrial IoT and Manufacturing Applications
    • 3.1.1 Predictive Maintenance Systems
    • 3.1.2 Quality Control and Inspection
    • 3.1.3 Real-time Analytics and Optimization
  • 3.2 Smartphone and Mobile Device Integration
    • 3.2.1 AI-Capable CPU Integration
    • 3.2.2 Specialized AI Accelerator Implementation
    • 3.2.3 Always-On Processing Capabilities
    • 3.2.4 AI PC Market
      • 3.2.4.1 Defining the AI PC
      • 3.2.4.2 AI PC Product Benchmarking
      • 3.2.4.3 Cutting-Edge Technologies in AI PCs
    • 3.2.5 AI Smartphone Market: Key Features and Flagship Phone Benchmarking
      • 3.2.5.1 AI Features in Flagship Smartphones
      • 3.2.5.2 Flagship Phone AI Processor Benchmarking
  • 3.3 Automotive and Transportation Systems
    • 3.3.1 SAE Levels of Autonomy and Edge AI Requirements
    • 3.3.2 Autonomous Driving Edge AI Processors
    • 3.3.3 Intelligent Cockpit Systems
  • 3.4 Humanoid Robot Applications
    • 3.4.1 Current Deployment Status and Applications
    • 3.4.2 Edge AI Processing Requirements for Humanoid Robots
    • 3.4.3 Edge AI Chip Companies Targeting Humanoid Robotics
  • 3.5 Smart Cities and Infrastructure Applications
  • 3.6 Healthcare and Wearable Device Integration
  • 3.7 Consumer Electronics and Home Automation
  • 3.8 Competitive Landscape and Market Players
    • 3.8.1 Established Semiconductor Giants
      • 3.8.1.1 NVIDIA
      • 3.8.1.2 Intel
      • 3.8.1.3 Qualcomm
      • 3.8.1.4 Xilinx
    • 3.8.2 AI-Focused Startup Companies
      • 3.8.2.1 Mythic
      • 3.8.2.2 Syntiant
      • 3.8.2.3 Kneron
      • 3.8.2.4 DeepX
    • 3.8.3 Cloud Provider Edge Solutions
      • 3.8.3.1 Google Edge TPU
      • 3.8.3.2 AWS Inferentia
  • 3.9 Market Drivers and Technology Trends
    • 3.9.1 Latency Requirements and Real-Time Processing Demands
    • 3.9.2 Data Privacy and Security Imperative Analysis
    • 3.9.3 Bandwidth Limitation and Connectivity Challenge Solutions
    • 3.9.4 IoT Device Proliferation Impact Assessment
    • 3.9.5 Edge-Cloud Computing Architecture Evolution
    • 3.9.6 Power Efficiency and Battery Life Optimization
    • 3.9.7 Autonomous System Processing Requirements
    • 3.9.8 Humanoid Robot Processing Requirements
    • 3.9.9 US-China Semiconductor Dynamics and Export Controls

4 COMPANY PROFILES 52 (54 company profiles)

5 REFERENCES

List of Tables

  • Table 1. Edge AI Chip Market Size by Application Segment, 2026-2036 (US$ Billions)
  • Table 2. Platform-Specific Revenue Analysis.
  • Table 3. Edge AI Chip Market Size by Geographic Region, 2026-2036 (US$ Billions)
  • Table 4. Key US Edge AI Chip Companies and Target Applications
  • Table 5. Key Chinese Edge AI Chip Companies and Target Applications
  • Table 6. Key European Edge AI Chip Companies and Target Applications
  • Table 7. Key Rest of World Edge AI Chip Companies and Target Applications
  • Table 8. TOPS/W Optimization Methodologies.
  • Table 9. Edge AI Processor Architecture Comparison
  • Table 10. Edge AI CPU Instruction Set Architecture Comparison
  • Table 11. Edge AI NPU Performance by Application Segment
  • Table 12. Semiconductor Foundry Landscape for Edge AI Chips
  • Table 13. Semiconductor Process Node Comparison for Edge AI Chips
  • Table 14. Advanced Packaging Technologies for Edge AI Chips
  • Table 15. Estimated Semiconductor Wafer Costs by Process Node
  • Table 16. Edge AI for Predictive Maintenance - Key Parameters by Industry
  • Table 17. AI PC Silicon Platform Comparison (2026)
  • Table 18. AI PC On-Device LLM Inference Capability (2026)
  • Table 19. Flagship Smartphone AI Processor Comparison (2026)
  • Table 20. Evolution of Apple Neural Engine AI Performance (2017-2026)
  • Table 21. AI Smartphone Market Segmentation (2026)
  • Table 22. SAE Levels of Driving Automation and Edge AI Compute Requirements
  • Table 23. Autonomous Driving Edge AI Processor Comparison (2026)
  • Table 24. Intelligent Cockpit AI Processing Requirements by Function
  • Table 25. Leading Humanoid Robot Programmes and Edge AI Requirements (2026)
  • Table 26. Humanoid Robot Edge AI Processing Requirements by Function
  • Table 27. Humanoid Robot Deployment Forecast by Environment (2026-2036)
  • Table 28. Edge AI Chip Market - Competitive Landscape Summary by Category
  • Table 29. Humanoid Robot Edge AI Chip Market Projections
  • Table 30. US Semiconductor Export Restriction Timeline and Impact on Edge AI Market
  • Table 31. Impact of Export Controls on Edge AI Chip Competitive Dynamics
  • Table 32. AMD AI chip range.
  • Table 33. Applications of CV3-AD685 in autonomous driving.
  • Table 34. Evolution of Apple Neural Engine.

List of Figures

  • Figure 1. AMD Radeon Instinct.
  • Figure 2. AMD Ryzen 7040.
  • Figure 3. Alveo V70.
  • Figure 4. Versal Adaptive SOC.
  • Figure 5. AMD's MI300 chip.
  • Figure 6. Ambarella's CV7 vision SoC
  • Figure 7. Cerebas WSE-2.
  • Figure 8. DeepX NPU DX-GEN1.
  • Figure 9. Encharge AI's EN100 M.2 card
  • Figure 10. Google TPU.
  • Figure 11. Colossus-TM MK2 GC200 IPU.
  • Figure 12. GreenWave's GAP8 and GAP9 processors.
  • Figure 13. Hailo's Hailo-10H edge AI accelerator
  • Figure 14. Innatera's Pulsar spiking neural processor
  • Figure 15. 11th Gen Intel-R Core-TM S-Series.
  • Figure 16. Pentonic 2000.
  • Figure 17. Azure Maia 100 and Cobalt 100 chips.
  • Figure 18. Mythic MP10304 Quad-AMP PCIe Card.
  • Figure 19. Nvidia H200 AI chip.
  • Figure 20. Grace Hopper Superchip.
  • Figure 21. Nvidia's Jetson Orin Nano
  • Figure 22. Cloud AI 100.
  • Figure 23. MLSoC-TM.
  • Figure 24. Synaptics' SL2610 multimodal edge AI processors
  • Figure 25. Grayskull.