세계의 딥러닝 칩셋 시장 : 유형별, 최종 사용자별 예측(2025-2030년)

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.

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