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深度學習晶片組市場：按類型、最終用戶分類 - 2025-2030 年全球預測

Deep Learning Chipset Market by Type (Application Specific Integrated Circuits, Central Processing Units, Field Programmable Gate Arrays), End-User (Aerospace & Defense, Automotive, Consumer Electronics) - Global Forecast 2025-2030
出版日期: | 出版商: 360iResearch | 英文 182 Pages | 商品交期: 最快1-2個工作天內

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2023年深度學習晶片組市場規模預計為102.3億美元，預計2024年將達到118.2億美元，複合年成長率為15.69%，預計到2030年將達到283.9億美元。

深度學習晶片組市場涵蓋人工智慧和硬體工程的動態交叉，推動運算效率、速度和功率的進步。這些晶片組（包括 GPU、TPU、神經型態晶片和 FPGA）可在汽車、醫療保健、金融和消費電子等多種行業中實現即時資料處理、複雜問題解決和自動化，為您的應用提供支援。對人工智慧主導解決方案不斷成長的需求凸顯了對深度學習晶片組的需求，這些解決方案可提高決策、預測分析和業務效率。最終用途範圍廣泛，涵蓋自動駕駛系統、醫學影像、個人化金融服務和智慧型設備，並受到物聯網技術擴散和機器學習演算法進步的推動。對關鍵成長要素的考察表明，終端用戶產業對人工智慧的採用增加、人工智慧研發投資的增加以及智慧基礎設施的興起正在產生關鍵影響。新的商機在於最佳化晶片結構以提高能源效率和速度，以及擴大深度學習和量子運算之間的相互作用，這有望使處理能力呈指數級成長。然而，市場面臨開發成本高、技術複雜性以及需要可擴展基礎設施來支援高階人工智慧工作負載等挑戰。此外，人們也擔心資料隱私和安全，這可能會影響市場信譽。創新的關鍵領域包括改進特定人工智慧應用的晶片設計、提高訓練神經網路的有效性，以及將深度學習功能融入提供利基市場和差異化潛力的邊緣設備中。為了實現業務成長，公司應專注於利用協作夥伴關係關係的敏捷策略，並投資於開發尖端晶片設計和人工智慧部署的技能。這是一個競爭激烈但前景廣闊的市場，適合解決人工智慧革命的技術限制和不斷成長的需求的開創性解決方案。

主要市場統計
基準年[2023] 102.3億美元
預測年份 [2024] 118.2億美元
預測年份 [2030] 283.9億美元
複合年成長率（%） 15.69%

市場動態：快速發展的深度學習晶片組市場的關鍵市場洞察

深度學習晶片組市場正因供需的動態交互作用而轉變。透過了解這些不斷變化的市場動態，公司可以準備好做出明智的投資決策、完善策略決策並抓住新的商機。全面了解這些趨勢可以幫助企業降低政治、地理、技術、社會和經濟領域的風險，同時也能幫助企業了解消費行為及其對製造業的影響。

  • 市場促進因素
    • 更廣泛地接受雲端基礎的技術
    • 擴大巨量資料分析在各產業的應用
    • 量子運算的興起和深度學習晶片在機器人領域的增強實施
  • 市場限制因素
    • 缺乏熟練的專業知識和訓練有素的專業人員
  • 市場機會
    • 持續需要開發具有人類意識的人工智慧系統
    • 自主機器人的開發
  • 市場挑戰
    • 收益降低且可用結構資料有限

波特五力：駕馭深度學習晶片組市場的策略工具

波特的五力框架是理解市場競爭格局的重要工具。波特的五力框架為評估公司的競爭地位和探索策略機會提供了清晰的方法。該框架可幫助公司評估市場動態並確定新業務的盈利。這些見解使公司能夠利用自己的優勢，解決弱點並避免潛在的挑戰，從而確保更強大的市場地位。

PESTLE分析：了解深度學習晶片組市場的外部影響

外部宏觀環境因素在塑造深度學習晶片組市場的性能動態方面發揮著至關重要的作用。對政治、經濟、社會、技術、法律和環境因素的分析提供了應對這些影響所需的資訊。透過調查 PESTLE 因素，公司可以更了解潛在的風險和機會。這種分析可以幫助公司預測法規、消費者偏好和經濟趨勢的變化，並幫助他們做出積極主動的決策。

市場佔有率分析 了解深度學習晶片組市場的競爭格局

對深度學習晶片組市場的詳細市場佔有率分析可以對供應商績效進行全面評估。公司可以透過比較收益、客戶群和成長率等關鍵指標來揭示其競爭地位。該分析揭示了市場集中、分散和整合的趨勢，為供應商提供了製定策略決策所需的洞察力，使他們能夠在日益激烈的競爭中佔有一席之地。

FPNV定位矩陣深度學習晶片組市場廠商表現評估

FPNV 定位矩陣是評估深度學習晶片組市場供應商的重要工具。此矩陣允許業務組織根據供應商的商務策略和產品滿意度評估供應商，從而做出符合其目標的明智決策。這四個象限使您能夠清晰、準確地分類供應商，並確定最能滿足您的策略目標的合作夥伴和解決方案。

策略分析和建議描繪了深度學習晶片組市場的成功之路

對於旨在加強其在全球市場的影響力的公司來說，深度學習晶片組市場的策略分析至關重要。透過審查關鍵資源、能力和績效指標，公司可以識別成長機會並努力改進。這種方法使您能夠克服競爭環境中的挑戰，利用新的商機，並取得長期成功。

本報告對市場進行了全面分析，涵蓋關鍵重點領域：

1. 市場滲透率：詳細檢視當前市場環境、主要企業的廣泛資料、評估其在市場中的影響力和整體影響力。

2. 市場開拓：辨識新興市場的成長機會，評估現有領域的擴張潛力，並提供未來成長的策略藍圖。

3. 市場多元化：分析近期產品發布、開拓地區、關鍵產業進展、塑造市場的策略投資。

4. 競爭評估與情報：徹底分析競爭格局，檢驗市場佔有率、業務策略、產品系列、認證、監理核准、專利趨勢、主要企業的技術進步等。

5.產品開發與創新：重點關注可望推動未來市場成長的最尖端科技、研發活動和產品創新。

我們也回答重要問題，以幫助相關人員做出明智的決策：

1.目前的市場規模和未來的成長預測是多少？

2. 哪些產品、區隔市場和地區提供最佳投資機會？

3.塑造市場的主要技術趨勢和監管影響是什麼？

4.主要廠商的市場佔有率和競爭地位如何？

5. 推動供應商市場進入和退出策略的收益來源和策略機會是什麼？

目錄

第1章 前言

第2章調查方法

第3章執行摘要

第4章市場概況

第5章市場洞察

  • 市場動態
    • 促進因素
      • 雲端基礎技術普及
      • 巨量資料分析的應用在各行業中不斷增加
      • 量子運算的興起和深度學習晶片在機器人領域的增強實施
    • 抑制因素
      • 缺乏熟練的專業知識和訓練有素的專業人員
    • 機會
      • 開發識別人類的人工智慧系統的需求仍然存在
      • 自主機器人新進展
    • 任務
      • 收益降低且可用結構資料有限
  • 市場區隔分析
  • 波特五力分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社群
    • 技術的
    • 合法地
    • 環境

第6章深度學習晶片組市場：依類型

  • 專用積體電路
  • 中央處理單元
  • 現場可程式閘陣列
  • 圖形處理單元

第 7 章 深度學習晶片組市場：依最終用戶分類

  • 航太和國防
  • 車
  • 家電
  • 衛生保健
  • 產業

第8章美洲深度學習晶片組市場

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美國

第9章亞太地區深度學習晶片組市場

  • 澳洲
  • 中國
  • 印度
  • 印尼
  • 日本
  • 馬來西亞
  • 菲律賓
  • 新加坡
  • 韓國
  • 台灣
  • 泰國
  • 越南

第10章歐洲、中東和非洲深度學習晶片組市場

  • 丹麥
  • 埃及
  • 芬蘭
  • 法國
  • 德國
  • 以色列
  • 義大利
  • 荷蘭
  • 奈及利亞
  • 挪威
  • 波蘭
  • 卡達
  • 俄羅斯
  • 沙烏地阿拉伯
  • 南非
  • 西班牙
  • 瑞典
  • 瑞士
  • 土耳其
  • 阿拉伯聯合大公國
  • 英國

第11章競爭格局

  • 2023 年市場佔有率分析
  • FPNV 定位矩陣，2023
  • 競爭情境分析
  • 戰略分析和建議

公司名單

  • Advanced Micro Devices, Inc.
  • ARM Holdings
  • Google LLC
  • Graphcore
  • Huawei Technologies
  • Intel Corporation
  • International Business Machines Corporation
  • LG Electronics
  • Mythic AI
  • NVIDIA Corporation
  • Qualcomm Technologies, Inc.
  • Samsung Electronics Co., Ltd.
  • Taiwan Semiconductor Manufacturing Company
  • Xilinx, Inc.
  • Zero ASIC Corporation
Product Code: MRR-4348D129F9D1

The Deep Learning Chipset Market was valued at USD 10.23 billion in 2023, expected to reach USD 11.82 billion in 2024, and is projected to grow at a CAGR of 15.69%, to USD 28.39 billion by 2030.

The deep learning chipset market encompasses a dynamic intersection of artificial intelligence and hardware engineering, driving advancements in computational efficiency, speed, and capability. These chipsets, including GPUs, TPUs, neuromorphic chips, and FPGAs, power applications across diverse industries like automotive, healthcare, finance, and consumer electronics by enabling real-time data processing, complex problem solving, and automation. The necessity for deep learning chipsets is underscored by the escalating demand for AI-driven solutions that enhance decision-making, predictive analytics, and operational efficiency. End-use scope is broad, spanning autonomous driving systems, medical imaging diagnostics, personalized finance services, and smart devices, bolstered by the proliferation of IoT technologies and advancements in machine learning algorithms. Insights into key growth factors reveal that the increasing adoption of AI in end-user industries, growing investments in AI research and development, and the rise of smart infrastructures are pivotal influences. Emerging opportunities lie in the optimization of chip architectures for energy efficiency and speed, as well as the growing intersections between deep learning and quantum computing, which promise exponential improvements in processing power. However, the market faces challenges, including high development costs, technical complexities, and the need for a scalable infrastructure to support advanced AI workloads. Additionally, there are concerns over data privacy and security, which may impact market confidence. Critical areas of innovation include improving chip design for specific AI applications, enhancing neural network training efficacy, and embedding deep learning capabilities into edge devices, offering potential for niche markets and differentiation. For business growth, companies should focus on agile strategies that leverage collaborative partnerships and invest in skill development for cutting-edge chip design and AI deployment. The market's nature is competitive yet full of promise, with a landscape ripe for pioneering solutions that address both the technical limitations and growing demands of the AI revolution.

KEY MARKET STATISTICS
Base Year [2023] USD 10.23 billion
Estimated Year [2024] USD 11.82 billion
Forecast Year [2030] USD 28.39 billion
CAGR (%) 15.69%

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Deep Learning Chipset Market

The Deep Learning Chipset Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

  • Market Drivers
    • Growing acceptance of cloud-based technology
    • Increasing application of big data analytics across industries
    • Rising quantum computing and enhanced implementation of deep learning chips in robotics
  • Market Restraints
    • Lack of skilled expertise and trained professional
  • Market Opportunities
    • Ongoing need to develop human-aware AI systems
    • Emerging development of autonomous robots
  • Market Challenges
    • Reduced return on investment and limited structural data available

Porter's Five Forces: A Strategic Tool for Navigating the Deep Learning Chipset Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Deep Learning Chipset Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Deep Learning Chipset Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Deep Learning Chipset Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Deep Learning Chipset Market

A detailed market share analysis in the Deep Learning Chipset Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Deep Learning Chipset Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Deep Learning Chipset Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Deep Learning Chipset Market

A strategic analysis of the Deep Learning Chipset Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Deep Learning Chipset Market, highlighting leading vendors and their innovative profiles. These include Advanced Micro Devices, Inc., ARM Holdings, Google LLC, Graphcore, Huawei Technologies, Intel Corporation, International Business Machines Corporation, LG Electronics, Mythic AI, NVIDIA Corporation, Qualcomm Technologies, Inc., Samsung Electronics Co., Ltd., Taiwan Semiconductor Manufacturing Company, Xilinx, Inc., and Zero ASIC Corporation.

Market Segmentation & Coverage

This research report categorizes the Deep Learning Chipset Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Based on Type, market is studied across Application Specific Integrated Circuits, Central Processing Units, Field Programmable Gate Arrays, and Graphics Processing Units.
  • Based on End-User, market is studied across Aerospace & Defense, Automotive, Consumer Electronics, Healthcare, and Industrial.
  • Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Segmentation & Coverage
  • 1.3. Years Considered for the Study
  • 1.4. Currency & Pricing
  • 1.5. Language
  • 1.6. Stakeholders

2. Research Methodology

  • 2.1. Define: Research Objective
  • 2.2. Determine: Research Design
  • 2.3. Prepare: Research Instrument
  • 2.4. Collect: Data Source
  • 2.5. Analyze: Data Interpretation
  • 2.6. Formulate: Data Verification
  • 2.7. Publish: Research Report
  • 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

  • 5.1. Market Dynamics
    • 5.1.1. Drivers
      • 5.1.1.1. Growing acceptance of cloud-based technology
      • 5.1.1.2. Increasing application of big data analytics across industries
      • 5.1.1.3. Rising quantum computing and enhanced implementation of deep learning chips in robotics
    • 5.1.2. Restraints
      • 5.1.2.1. Lack of skilled expertise and trained professional
    • 5.1.3. Opportunities
      • 5.1.3.1. Ongoing need to develop human-aware AI systems
      • 5.1.3.2. Emerging development of autonomous robots
    • 5.1.4. Challenges
      • 5.1.4.1. Reduced return on investment and limited structural data available
  • 5.2. Market Segmentation Analysis
  • 5.3. Porter's Five Forces Analysis
    • 5.3.1. Threat of New Entrants
    • 5.3.2. Threat of Substitutes
    • 5.3.3. Bargaining Power of Customers
    • 5.3.4. Bargaining Power of Suppliers
    • 5.3.5. Industry Rivalry
  • 5.4. PESTLE Analysis
    • 5.4.1. Political
    • 5.4.2. Economic
    • 5.4.3. Social
    • 5.4.4. Technological
    • 5.4.5. Legal
    • 5.4.6. Environmental

6. Deep Learning Chipset Market, by Type

  • 6.1. Introduction
  • 6.2. Application Specific Integrated Circuits
  • 6.3. Central Processing Units
  • 6.4. Field Programmable Gate Arrays
  • 6.5. Graphics Processing Units

7. Deep Learning Chipset Market, by End-User

  • 7.1. Introduction
  • 7.2. Aerospace & Defense
  • 7.3. Automotive
  • 7.4. Consumer Electronics
  • 7.5. Healthcare
  • 7.6. Industrial

8. Americas Deep Learning Chipset Market

  • 8.1. Introduction
  • 8.2. Argentina
  • 8.3. Brazil
  • 8.4. Canada
  • 8.5. Mexico
  • 8.6. United States

9. Asia-Pacific Deep Learning Chipset Market

  • 9.1. Introduction
  • 9.2. Australia
  • 9.3. China
  • 9.4. India
  • 9.5. Indonesia
  • 9.6. Japan
  • 9.7. Malaysia
  • 9.8. Philippines
  • 9.9. Singapore
  • 9.10. South Korea
  • 9.11. Taiwan
  • 9.12. Thailand
  • 9.13. Vietnam

10. Europe, Middle East & Africa Deep Learning Chipset Market

  • 10.1. Introduction
  • 10.2. Denmark
  • 10.3. Egypt
  • 10.4. Finland
  • 10.5. France
  • 10.6. Germany
  • 10.7. Israel
  • 10.8. Italy
  • 10.9. Netherlands
  • 10.10. Nigeria
  • 10.11. Norway
  • 10.12. Poland
  • 10.13. Qatar
  • 10.14. Russia
  • 10.15. Saudi Arabia
  • 10.16. South Africa
  • 10.17. Spain
  • 10.18. Sweden
  • 10.19. Switzerland
  • 10.20. Turkey
  • 10.21. United Arab Emirates
  • 10.22. United Kingdom

11. Competitive Landscape

  • 11.1. Market Share Analysis, 2023
  • 11.2. FPNV Positioning Matrix, 2023
  • 11.3. Competitive Scenario Analysis
  • 11.4. Strategy Analysis & Recommendation

Companies Mentioned

  • 1. Advanced Micro Devices, Inc.
  • 2. ARM Holdings
  • 3. Google LLC
  • 4. Graphcore
  • 5. Huawei Technologies
  • 6. Intel Corporation
  • 7. International Business Machines Corporation
  • 8. LG Electronics
  • 9. Mythic AI
  • 10. NVIDIA Corporation
  • 11. Qualcomm Technologies, Inc.
  • 12. Samsung Electronics Co., Ltd.
  • 13. Taiwan Semiconductor Manufacturing Company
  • 14. Xilinx, Inc.
  • 15. Zero ASIC Corporation

LIST OF FIGURES

  • FIGURE 1. DEEP LEARNING CHIPSET MARKET RESEARCH PROCESS
  • FIGURE 2. DEEP LEARNING CHIPSET MARKET SIZE, 2023 VS 2030
  • FIGURE 3. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, 2018-2030 (USD MILLION)
  • FIGURE 4. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 5. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 6. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2023 VS 2030 (%)
  • FIGURE 7. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 8. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2023 VS 2030 (%)
  • FIGURE 9. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 10. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 11. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 12. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2023 VS 2030 (%)
  • FIGURE 13. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 14. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 15. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 16. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 17. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 18. DEEP LEARNING CHIPSET MARKET SHARE, BY KEY PLAYER, 2023
  • FIGURE 19. DEEP LEARNING CHIPSET MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

  • TABLE 1. DEEP LEARNING CHIPSET MARKET SEGMENTATION & COVERAGE
  • TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
  • TABLE 3. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, 2018-2030 (USD MILLION)
  • TABLE 4. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 5. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 6. DEEP LEARNING CHIPSET MARKET DYNAMICS
  • TABLE 7. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 8. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY APPLICATION SPECIFIC INTEGRATED CIRCUITS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 9. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY CENTRAL PROCESSING UNITS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 10. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY FIELD PROGRAMMABLE GATE ARRAYS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 11. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY GRAPHICS PROCESSING UNITS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 12. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 13. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY AEROSPACE & DEFENSE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 14. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY AUTOMOTIVE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 15. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY CONSUMER ELECTRONICS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 16. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY HEALTHCARE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 17. GLOBAL DEEP LEARNING CHIPSET MARKET SIZE, BY INDUSTRIAL, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 18. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 19. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 20. AMERICAS DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 21. ARGENTINA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 22. ARGENTINA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 23. BRAZIL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 24. BRAZIL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 25. CANADA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 26. CANADA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 27. MEXICO DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 28. MEXICO DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 29. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 30. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 31. UNITED STATES DEEP LEARNING CHIPSET MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
  • TABLE 32. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 33. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 34. ASIA-PACIFIC DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 35. AUSTRALIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 36. AUSTRALIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 37. CHINA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 38. CHINA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 39. INDIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 40. INDIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 41. INDONESIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 42. INDONESIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 43. JAPAN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 44. JAPAN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 45. MALAYSIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 46. MALAYSIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 47. PHILIPPINES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 48. PHILIPPINES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 49. SINGAPORE DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 50. SINGAPORE DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 51. SOUTH KOREA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 52. SOUTH KOREA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 53. TAIWAN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 54. TAIWAN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 55. THAILAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 56. THAILAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 57. VIETNAM DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 58. VIETNAM DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 59. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 60. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 61. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 62. DENMARK DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 63. DENMARK DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 64. EGYPT DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 65. EGYPT DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 66. FINLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 67. FINLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 68. FRANCE DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 69. FRANCE DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 70. GERMANY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 71. GERMANY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 72. ISRAEL DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 73. ISRAEL DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 74. ITALY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 75. ITALY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 76. NETHERLANDS DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 77. NETHERLANDS DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 78. NIGERIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 79. NIGERIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 80. NORWAY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 81. NORWAY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 82. POLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 83. POLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 84. QATAR DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 85. QATAR DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 86. RUSSIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 87. RUSSIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 88. SAUDI ARABIA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 89. SAUDI ARABIA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 90. SOUTH AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 91. SOUTH AFRICA DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 92. SPAIN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 93. SPAIN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 94. SWEDEN DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 95. SWEDEN DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 96. SWITZERLAND DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 97. SWITZERLAND DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 98. TURKEY DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 99. TURKEY DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 100. UNITED ARAB EMIRATES DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 101. UNITED ARAB EMIRATES DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 102. UNITED KINGDOM DEEP LEARNING CHIPSET MARKET SIZE, BY TYPE, 2018-2030 (USD MILLION)
  • TABLE 103. UNITED KINGDOM DEEP LEARNING CHIPSET MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 104. DEEP LEARNING CHIPSET MARKET SHARE, BY KEY PLAYER, 2023
  • TABLE 105. DEEP LEARNING CHIPSET MARKET, FPNV POSITIONING MATRIX, 2023