邊緣人工智慧晶片：技術、市場及預測（2026-2036）

Edge AI Chips: Technologies, Markets, and Forecasts 2026-2036

出版日期: 2026年02月23日 | 出版商:

Future Markets, Inc. | 英文 126 Pages, 34 Tables, 25 Figures | 訂單完成後即時交付

價格

簡介目錄圖表

隨著人工智慧從集中式雲端資料中心遷移到資料生成設備（例如智慧型手機、汽車、機器人、工業感測器和個人電腦），全球邊緣人工智慧晶片市場正經歷前所未有的成長。邊緣人工智慧晶片，包括神經網路處理單元 (NPU)、圖形處理器 (GPU) 和中央處理器 (CPU)，使設備能夠在本地做出智慧決策，而無需依賴雲端連接。這消除了延遲，增強了資料隱私，降低了頻寬需求，並支援在安全關鍵型應用中實現即時自主運行。預計到 2036 年，邊緣人工智慧晶片市場規模將超過 800 億美元，主要驅動力來自五大關鍵應用領域：汽車、人工智慧智慧型手機、人工智慧個人電腦、人形機器人和用於預測性維護的人工智慧感測器。

汽車產業是最大的成長機會之一，隨著 SAE L2+ 級到 L3 級自動駕駛技術的進步，法律責任從駕駛員轉移到汽車製造商 (OEM)，因此對邊緣人工智慧運算能力的需求顯著增強。智慧座艙系統是汽車產業的另一個細分市場，需要專門的人工智慧處理能力來實現語音助理、駕駛監控、手勢辨識和擴增實境顯示等功能。汽車產業與消費性電子產業一樣，將自動駕駛和智慧座艙功能結合，成為兩大邊緣人工智慧晶片市場之一。

人工智慧智慧型手機在邊緣人工智慧晶片市場中佔主導地位，截至2026年1月，所有主要OEM廠商都已在其旗艦設備中整合人工智慧功能。人工智慧PC的專用人工智慧處理能力超過40 TOPS，在2025年僅佔新PC銷量的不到10%，但預計到2030年代初將佔新PC銷量的大部分。英特爾、高通、蘋果和AMD的平台預計將爭奪市場佔有率。

人形機器人被定位為一個發展中但極具前景的應用領域。截至2026年，它們在汽車製造領域的應用正在不斷擴大，預計未來十年將擴展到安防、監控和家庭環境領域。隨著超出目前揀貨和物流作業的複雜任務數量不斷增加，每個機器人所需的 AI 運算能力預計將顯著提升。

本報告分析了全球邊緣 AI 晶片市場，提供了 54 家公司的詳細概況，包括成熟的半導體公司、專注於 AI 的新創公司和雲端供應商的邊緣解決方案，以及技術架構、應用市場、競爭格局和地理預測。

NPU實現
SoC整合策略
能源效率與效能最佳化
- 低於 7W 的散熱要求
- TOPS/W 最佳化技術
- 模型壓縮與量化
模擬計算與記憶體處理
專用 NPU 架構
基於 GPU 的邊緣解決方案與專用 DPU 的比較
邊緣 AI 晶片供應鏈分析
- CPU 供應鏈
- NPU 供應鏈
- GPU 供應鏈
- 代工與製造供應鏈
尖端半導體製造流程概述
- 當前尖端製程（3nm、4nm）
- 下一代製程（2nm）
- 先進封裝技術
- 製程技術對邊緣 AI 晶片成本的影響

第三章應用市場分析

工業物聯網與製造應用
- 預測性維護系統
- 品質控制與檢測
- 即時分析與最佳化
智慧型手機和行動裝置整合
- AI賦能的CPU整合
- 專用AI加速器實現
- 連續處理功能
- AI PC市場
- AI智慧型手機市場：主要功能與旗艦手機基準測試
汽車與交通運輸系統
- SAE自動駕駛等級和邊緣AI要求
- 自動駕駛邊緣AI處理器
- 智慧座艙系統
人形機器人應用
- 目前部署狀態和應用
- 人形機器人邊緣AI處理要求
- 面向人形機器人的邊緣AI晶片公司機器人
智慧城市與基礎建設應用
醫療保健與穿戴式裝置整合
消費性電子與智慧家庭
競爭格局與市場參與者
- 現有主要半導體公司
- 專注於人工智慧的新創公司
- 雲端提供者邊緣解決方案
市場驅動因素與技術趨勢
- 延遲要求與即時處理需求
- 資料隱私與安全需求分析
- 解決頻寬限制與連線挑戰
- 評估物聯網設備激增的影響
- 邊緣雲端運算架構的演進
- 優化電源效率與電池壽命
- 自主系統處理需求
- 人形機器人處理需求
- 中美半導體市場動態與出口限制

第四章公司簡介（54 家公司簡介）

第五章參考資料

簡介目錄圖表

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......

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

簡介目錄圖表

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.