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市場調查報告書
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2000449

人工智慧晶片設計市場預測至2034年－按晶片類型、架構、製程節點、技術、應用、最終用戶和地區分類的全球分析

AI Chip Design Market Forecasts to 2034 - Global Analysis By Chip Type, Architecture, Process Node, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球人工智慧晶片設計市場規模將達到 46.5 億美元，在預測期內將以 34.6% 的複合年成長率成長，到 2034 年將達到 501.7 億美元。

AI晶片設計是指應用人工智慧（AI）和機器學習技術來自動化、最佳化和加速半導體設計流程。這使得工程師能夠以比傳統方法更快、更精確的速度探索架構、佈局電路、檢驗並最佳化功耗和效能。透過分析海量設計資料集，AI驅動的工具能夠縮短開發週期、降低成本並提高晶片的效率和可靠性。這種方法在開發用於資料中心、邊緣設備、自主系統和下一代運算平台的複雜高效能處理器方面正變得越來越重要。

對人工智慧工作負載的爆炸性需求

人工智慧在資料中心、雲端運算、自動駕駛汽車和邊緣設備等領域的快速應用，正顯著推動人工智慧晶片設計市場的發展。對高效能運算、即時資料處理和大規模模型訓練日益成長的需求，催生了對高度最佳化、高能效半導體架構的迫切需求。人工智慧驅動的設計工具能夠加快原型製作速度，提升晶片效能，幫助企業滿足不斷變化的工作負載需求。隨著生成式人工智慧和先進分析技術的不斷發展，對智慧晶片設計解決方案的需求也急劇成長。

高昂的開發和實施成本

設計人工智慧晶片需要對先進的電子設計自動化工具、熟練的工程人員和高效能運算基礎設施進行大量投資。將人工智慧整合到傳統的半導體工作流程中需要對製程進行大量的重新配置和檢驗。此外，採用先進製程節點進行製造會增加生產成本。這些高昂的初始成本和營運成本對中小企業來說都是障礙，限制了人工智慧晶片的普及應用，並減緩了創新步伐。

現代晶片日益複雜

電晶體小型化和異構整合推動半導體架構日益複雜，為人工智慧晶片設計解決方案帶來了巨大的發展機會。現代處理器需要在功耗、熱效率和效能之間進行精細的最佳化平衡。人工智慧演算法能夠分析海量的設計組合，識別最優佈局，並高精度地預測效能結果。隨著晶片結構向系統晶片晶片 (SoC) 和多晶片設計演進，人工智慧驅動的自動化對於高效且具競爭力地應對複雜性至關重要。

複雜的檢驗和確認

確保人工智慧生成的晶片設計的準確性、可靠性和功能安全性仍然是一項重大挑戰。半導體產品必須滿足嚴格的監管和性能標準，這需要大量的檢驗和確認程序。如果未經徹底測試，基於人工智慧的設計輸出可能會導致不可預測的設計異常。嚴格的模擬、合規性測試和品質保證要求可能會延長開發週期並增加營運風險，這可能會阻礙全自動晶片設計的廣泛應用。

新冠疫情的影響：

新冠疫情初期，封鎖和物流限制擾亂了半導體供應鏈、生產計畫以及研發活動。然而，疫情也加速了全球數位轉型、遠端運算和雲端運算的普及。對人工智慧驅動服務、線上平台和數據密集應用的日益依賴，增強了對先進半導體技術的長期需求。在疫情後的復甦階段，供應鏈韌性和自動化成為關注焦點，間接推動了對人工智慧晶片設計解決方案的投資，以提高效率和競爭力。

在預測期內，深度學習晶片細分市場預計將佔據最大的市場佔有率。

預計在預測期內，深度學習晶片領域將佔據最大的市場佔有率，這主要得益於對更快人工智慧訓練和推理工作負載日益成長的需求。這些晶片針對神經網路運算、高並行性和節能運行進行了專門最佳化。生成式人工智慧、自然語言處理和電腦視覺應用的激增正在推動對專用處理器的需求。人工智慧驅動的晶片設計將進一步提升該領域的架構效率和效能可擴展性。

預計在預測期內，醫療保健產業將呈現最高的複合年成長率。

在預測期內，醫療保健產業預計將呈現最高的成長率，這主要得益於人工智慧驅動的診斷、醫學影像、預測分析和個人化醫療解決方案的日益普及。醫療保健應用需要安全、高效能的處理能力，以進行即時數據分析和邊緣醫療設備運作。人工智慧晶片設計能夠實現針對醫療保健環境最佳化的低延遲半導體解決方案。數位醫療基礎設施的擴展以及對人工智慧整合的監管支持，進一步推動了該領域的需求。

市佔率最大的地區：

在預測期內，亞太地區預計將佔據最大的市場佔有率，這主要得益於其強大的半導體製造基礎、蓬勃發展的電子產業以及在人工智慧研究領域的大量投入。中國、韓國、台灣和日本等國家和地區是晶片製造和創新的主要驅動力。政府支持國內半導體能力建設的舉措以及對人工智慧驅動型家用電子電器日益成長的需求，進一步鞏固了該地區的市場主導地位。

複合年成長率最高的地區：

在預測期內，北美預計將展現出最高的複合年成長率，這主要得益於強勁的技術創新、許多大型半導體設計公司的存在以及對人工智慧研究的大量投資。領先的雲端服務供應商、人工智慧Start-Ups以及先進的研發現狀系統正在加速推動對人工智慧驅動的晶片設計解決方案的需求。支持性的政策框架、對半導體自給自足能力的投入增加以及生成式人工智慧技術的快速應用，都為該地區的持續成長勢頭做出了貢獻。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧晶片設計市場：按晶片類型分類

  • 圖形處理單元
  • 專用積體電路
  • 現場可程式閘陣列
  • 中央處理器
  • 神經形態晶片
  • 其他類型的小費

第6章：全球人工智慧晶片設計市場：依架構分類

  • 馮諾伊曼
  • 記憶體內運算
  • 平行處理

第7章 全球人工智慧晶片設計市場：依製程節點分類

  • 7奈米或更小
  • 10 nm
  • 14 nm
  • 28奈米或更大

第8章：全球人工智慧晶片設計市場：按技術分類

  • 機器學習晶片
  • 用於深度學習的晶片
  • 自然語言處理晶片
  • 電腦視覺晶片

第9章 全球人工智慧晶片設計市場：按應用領域分類

  • 資料中心
  • 家用電子產品
  • 車
  • 衛生保健
  • 產業
  • 機器人技術

第10章：全球人工智慧晶片設計市場：以最終用戶分類

  • 雲端服務供應商
  • 公司
  • 政府/國防
  • 研究機構

第11章 全球人工智慧晶片設計市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • NVIDIA
  • Intel
  • Advanced Micro Devices（AMD）
  • Qualcomm
  • Huawei Technologies
  • Apple
  • Samsung Electronics
  • Alphabet
  • IBM
  • Graphcore
  • Hailo Technologies
  • Cerebras Systems
  • Mythic Inc.
  • MediaTek
  • Cambricon Technologies
Product Code: SMRC34475

According to Stratistics MRC, the Global AI Chip Design Market is accounted for $4.65 billion in 2026 and is expected to reach $50.17 billion by 2034 growing at a CAGR of 34.6% during the forecast period. AI chip design refers to the application of artificial intelligence and machine learning techniques to automate, optimize, and accelerate semiconductor design processes. It enables engineers to improve architecture exploration, circuit layout, verification, and power-performance optimization with greater speed and accuracy than traditional methods. By analyzing vast design datasets, AI-driven tools reduce development time, lower costs, and enhance chip efficiency and reliability. This approach is increasingly critical for developing complex, high-performance processors used in data centers, edge devices, autonomous systems, and next generation computing platforms.

Market Dynamics:

Driver:

Explosive demand for AI workloads

The rapid expansion of artificial intelligence applications across data centers, cloud computing, autonomous vehicles, and edge devices is significantly driving the AI chip design market. Increasing demand for high performance computing, real time data processing and large-scale model training requires highly optimized and power efficient semiconductor architectures. AI-driven design tools enable faster prototyping and improved silicon performance, allowing companies to meet evolving workload requirements. As generative AI and advanced analytics continue to scale, the need for intelligent chip design solutions accelerates substantially.

Restraint:

High development and implementation costs

AI chip design involves substantial investment in advanced electronic design automation tools, skilled engineering talent, and high performance computing infrastructure. The integration of AI into conventional semiconductor workflows requires significant process reconfiguration and validation efforts. Additionally, fabrication at advanced process nodes increases production expenses. These high upfront and operational costs create barriers for small and mid-sized enterprises, limiting broader adoption and slowing innovation.

Opportunity:

Rising complexity of modern chips

The increasing complexity of semiconductor architectures, driven by shrinking transistor nodes and heterogeneous integration, presents strong growth opportunities for AI chip design solutions. Modern processors require advanced optimization for power, thermal efficiency, and performance balance. AI algorithms can analyze vast design permutations, identify optimal layouts, and predict performance outcomes with high precision. As chip architectures evolve toward system on chip and multi chiplet designs, AI enabled automation becomes essential for managing complexity efficiently and competitively.

Threat:

Complex verification and validation

Ensuring accuracy, reliability, and functional safety in AI generated chip designs remains a significant challenge. Semiconductor products must meet strict regulatory and performance standards, requiring extensive verification and validation procedures. AI-based design outputs may introduce unpredictable design anomalies if not thoroughly tested. The need for rigorous simulation, compliance testing, and quality assurance increases development timelines and operational risks, potentially hindering widespread adoption of fully autonomous chip design.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted semiconductor supply chains, fabrication schedules, and R&D operations due to lockdowns and logistical constraints. However, it also accelerated digital transformation, remote computing, and cloud adoption globally. Increased reliance on AI-driven services, online platforms, and data intensive applications strengthened long-term demand for advanced semiconductor technologies. Post-pandemic recovery has emphasized supply chain resilience and automation, indirectly boosting investment in AI-enabled chip design solutions to enhance efficiency and competitiveness.

The deep learning chips segment is expected to be the largest during the forecast period

The deep learning chips segment is expected to account for the largest market share during the forecast period, due to the growing demand for accelerated AI training and inference workloads. These chips are specifically optimized for neural network computations, high parallel processing, and energy efficient operations. The surge in generative AI, natural language processing, and computer vision applications drives the need for specialized processors. AI assisted chip design further enhances architectural efficiency and performance scalability in this segment.

The healthcare segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare segment is predicted to witness the highest growth rate, due to increasing adoption of AI-powered diagnostics, medical imaging, predictive analytics, and personalized medicine solutions. Healthcare applications require secure, high-performance processing for real time data analysis and edge-based medical devices. AI chip design enables optimized, low-latency semiconductor solutions tailored for medical environments. Growing digital health infrastructure and regulatory support for AI integration further strengthen demand in this sector.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, due to its strong semiconductor manufacturing base, expanding electronics industry, and substantial investments in AI research. Countries such as China, South Korea, Taiwan, and Japan are major contributors to chip fabrication and innovation. Government initiatives supporting domestic semiconductor capabilities and rising demand for AI-enabled consumer electronics further reinforce regional market dominance.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to strong technological innovation, leading semiconductor design companies, and significant investments in artificial intelligence research. The presence of major cloud service providers, AI startups, and advanced R&D ecosystems accelerates demand for AI-driven chip design solutions. Supportive policy frameworks, increased funding for semiconductor independence, and rapid adoption of generative AI technologies contribute to sustained regional growth momentum.

Key players in the market

Some of the key players in AI Chip Design Market include NVIDIA, Intel, Advanced Micro Devices (AMD), Qualcomm, Huawei Technologies, Apple, Samsung Electronics, Alphabet, IBM, Graphcore, Hailo Technologies, Cerebras Systems, Mythic Inc., MediaTek and Cambricon Technologies.

Key Developments:

In December 2025, Samsung Electronics announced that it will introduce a new Samsung interior fit installation service that expands its products and strengthens customer benefits to customer response. Samsung's interior fit installation service is a service that provides customers with the removal of existing furniture stores, construction, and product installation at once according to their new purchases or home appliances.

In October 2025, OpenAI, Samsung Electronics, Samsung SDS, Samsung C&T and Samsung Heavy Industries announced a letter of intent (LOI) for their strategic partnership to accelerate advancements in global AI data center infrastructure and develop future technologies together in relevant fields.

Chip Types Covered:

  • Graphics Processing Unit
  • Application Specific Integrated Circuit
  • Field Programmable Gate Array
  • Central Processing Unit
  • Neuromorphic Chips
  • Other Chip Types

Architectures Covered:

  • Von Neumann
  • In Memory Computing
  • Parallel Processing

Process Nodes Covered:

  • 7 nm and Below
  • 10 nm
  • 14 nm
  • 28 nm and Above

Technologies Covered:

  • Machine Learning Chips
  • Deep Learning Chips
  • Natural Language Processing Chips
  • Computer Vision Chips

Applications Covered:

  • Data Centers
  • Consumer Electronics
  • Automotive
  • Healthcare
  • Industrial
  • Robotics

End Users Covered:

  • Cloud Service Providers
  • Enterprises
  • Government & Defense
  • Research Institutions

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI Chip Design Market, By Chip Type

  • 5.1 Graphics Processing Unit
  • 5.2 Application Specific Integrated Circuit
  • 5.3 Field Programmable Gate Array
  • 5.4 Central Processing Unit
  • 5.5 Neuromorphic Chips
  • 5.6 Other Chip Types

6 Global AI Chip Design Market, By Architecture

  • 6.1 Von Neumann
  • 6.2 In Memory Computing
  • 6.3 Parallel Processing

7 Global AI Chip Design Market, By Process Node

  • 7.1 7 nm and Below
  • 7.2 10 nm
  • 7.3 14 nm
  • 7.4 28 nm and Above

8 Global AI Chip Design Market, By Technology

  • 8.1 Machine Learning Chips
  • 8.2 Deep Learning Chips
  • 8.3 Natural Language Processing Chips
  • 8.4 Computer Vision Chips

9 Global AI Chip Design Market, By Application

  • 9.1 Data Centers
  • 9.2 Consumer Electronics
  • 9.3 Automotive
  • 9.4 Healthcare
  • 9.5 Industrial
  • 9.6 Robotics

10 Global AI Chip Design Market, By End User

  • 10.1 Cloud Service Providers
  • 10.2 Enterprises
  • 10.3 Government & Defense
  • 10.4 Research Institutions

11 Global AI Chip Design Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA
  • 14.2 Intel
  • 14.3 Advanced Micro Devices (AMD)
  • 14.4 Qualcomm
  • 14.5 Huawei Technologies
  • 14.6 Apple
  • 14.7 Samsung Electronics
  • 14.8 Alphabet
  • 14.9 IBM
  • 14.10 Graphcore
  • 14.11 Hailo Technologies
  • 14.12 Cerebras Systems
  • 14.13 Mythic Inc.
  • 14.14 MediaTek
  • 14.15 Cambricon Technologies

List of Tables

  • Table 1 Global AI Chip Design Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Chip Design Market Outlook, By Chip Type (2023-2034) ($MN)
  • Table 3 Global AI Chip Design Market Outlook, By Graphics Processing Unit (2023-2034) ($MN)
  • Table 4 Global AI Chip Design Market Outlook, By Application Specific Integrated Circuit (2023-2034) ($MN)
  • Table 5 Global AI Chip Design Market Outlook, By Field Programmable Gate Array (2023-2034) ($MN)
  • Table 6 Global AI Chip Design Market Outlook, By Central Processing Unit (2023-2034) ($MN)
  • Table 7 Global AI Chip Design Market Outlook, By Neuromorphic Chips (2023-2034) ($MN)
  • Table 8 Global AI Chip Design Market Outlook, By Other Chip Types (2023-2034) ($MN)
  • Table 9 Global AI Chip Design Market Outlook, By Architecture (2023-2034) ($MN)
  • Table 10 Global AI Chip Design Market Outlook, By Von Neumann (2023-2034) ($MN)
  • Table 11 Global AI Chip Design Market Outlook, By In Memory Computing (2023-2034) ($MN)
  • Table 12 Global AI Chip Design Market Outlook, By Parallel Processing (2023-2034) ($MN)
  • Table 13 Global AI Chip Design Market Outlook, By Process Node (2023-2034) ($MN)
  • Table 14 Global AI Chip Design Market Outlook, By 7 nm and Below (2023-2034) ($MN)
  • Table 15 Global AI Chip Design Market Outlook, By 10 nm (2023-2034) ($MN)
  • Table 16 Global AI Chip Design Market Outlook, By 14 nm (2023-2034) ($MN)
  • Table 17 Global AI Chip Design Market Outlook, By 28 nm and Above (2023-2034) ($MN)
  • Table 18 Global AI Chip Design Market Outlook, By Technology (2023-2034) ($MN)
  • Table 19 Global AI Chip Design Market Outlook, By Machine Learning Chips (2023-2034) ($MN)
  • Table 20 Global AI Chip Design Market Outlook, By Deep Learning Chips (2023-2034) ($MN)
  • Table 21 Global AI Chip Design Market Outlook, By Natural Language Processing Chips (2023-2034) ($MN)
  • Table 22 Global AI Chip Design Market Outlook, By Computer Vision Chips (2023-2034) ($MN)
  • Table 23 Global AI Chip Design Market Outlook, By Application (2023-2034) ($MN)
  • Table 24 Global AI Chip Design Market Outlook, By Data Centers (2023-2034) ($MN)
  • Table 25 Global AI Chip Design Market Outlook, By Consumer Electronics (2023-2034) ($MN)
  • Table 26 Global AI Chip Design Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 27 Global AI Chip Design Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 28 Global AI Chip Design Market Outlook, By Industrial (2023-2034) ($MN)
  • Table 29 Global AI Chip Design Market Outlook, By Robotics (2023-2034) ($MN)
  • Table 30 Global AI Chip Design Market Outlook, By End User (2023-2034) ($MN)
  • Table 31 Global AI Chip Design Market Outlook, By Cloud Service Providers (2023-2034) ($MN)
  • Table 32 Global AI Chip Design Market Outlook, By Enterprises (2023-2034) ($MN)
  • Table 33 Global AI Chip Design Market Outlook, By Government & Defense (2023-2034) ($MN)
  • Table 34 Global AI Chip Design Market Outlook, By Research Institutions (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.