한글목차

세계의 AI 컴퓨팅 하드웨어 시장의 미래는 BFSI, 자동차, 헬스케어, IT·통신, 항공우주·방위, 에너지·유틸리티, 정부·공공 서비스의 각 시장에서의 기회로 유망시되고 있습니다. 세계의 AI 컴퓨팅 하드웨어 시장은 2025-2031년에 CAGR 25.3%로 성장할 것으로 예상됩니다. 이 시장의 주요 촉진요인은 자동화 및 효율화를 위한 다양한 산업에서의 AI 통합 증가, 데이터 처리에 대한 수요 증가, 빅데이터 및 분석을 관리하기 위한 고성능 컴퓨팅에 대한 수요 증가 등입니다.

• Lucintel의 예측에 따르면 유형별로는 독립형 비전 프로세서가 예측 기간 중 가장 높은 성장세를 보일 것으로 예상됩니다.
• 용도별로는 BFSI가 가장 높은 성장이 예상됩니다.
• 지역별로는 아시아태평양이 예측 기간 중 가장 높은 성장을 보일 것으로 예상됩니다.

AI 컴퓨팅 하드웨어 시장에서의 전략적 성장 기회

AI 컴퓨팅 하드웨어 시장에는 다양한 용도에서 몇 가지 전략적 성장 기회가 있습니다. 이러한 기회를 활용하면 AI 하드웨어 분야의 혁신과 확장을 촉진할 수 있습니다.

• 자율주행차로의 확장 : AI 컴퓨팅 하드웨어는 센서와 카메라의 실시간 데이터를 처리하기 위해 자율주행차에서 점점 더 많이 사용되고 있습니다. 이에 따라 자동차 업계는 고성능, 고신뢰성 하드웨어 솔루션에 대한 수요가 증가하고 있습니다.
• 데이터센터의 성장 : AI 워크로드를 지원하는 데이터센터의 확장은 AI 하드웨어 공급업체에게 기회를 제공합니다. 처리 능력과 스토리지에 대한 수요 증가는 고급 컴퓨팅 인프라에 대한 투자를 촉진합니다.
• AI 기반 헬스케어 솔루션 개발 : AI 컴퓨팅 하드웨어는 진단 툴 및 맞춤형 의료와 같은 헬스케어 솔루션 개발에 중요한 역할을 하고 있습니다. 이 분야의 성장은 의료 분야에 특화된 전용 하드웨어를 개발할 수 있는 기회를 제공합니다.
• 스마트 시티의 발전 : AI 하드웨어는 교통 관리 및 공공안전 시스템과 같은 스마트 시티 구상에 필수적입니다. 효율적이고 확장 가능한 컴퓨팅 솔루션에 대한 요구가 이 용도 분야의 성장을 주도하고 있습니다.
• 5G 네트워크와의 통합 : 5G 네트워크의 구축은 고속 데이터 처리와 저지연 용도를 지원하는 AI 하드웨어에 대한 기회를 창출하고 있으며, 5G와의 통합은 다양한 분야의 AI 솔루션의 능력을 향상시킵니다.

이러한 전략적 성장 기회는 AI 컴퓨팅 하드웨어의 다양한 용도를 강조하고, 시장에서의 혁신과 확장 가능성을 강조합니다. 이러한 기회를 활용하면 AI 하드웨어 산업의 발전과 성장을 가속할 수 있습니다.

AI 컴퓨팅 하드웨어 시장 성장 촉진요인 및 과제

AI 컴퓨팅 하드웨어 시장은 다양한 기술적, 경제적, 규제적 요인의 영향을 받습니다. 이러한 시장 성장 촉진요인 및 과제를 이해하는 것은 시장을 탐색하고 기회를 활용하기 위해 필수적입니다.

AI 컴퓨팅 하드웨어 시장 성장 촉진요인은 다음과 같습니다:

• AI 알고리즘의 발전 : AI 알고리즘의 고도화는 복잡한 계산을 처리할 수 있는 강력한 컴퓨팅 하드웨어에 대한 수요를 증가시켜 AI 하드웨어에 대한 기술 혁신과 투자를 촉진합니다.
• 데이터 양 증가 : 데이터의 급격한 증가로 인해 대규모 데이터세트를 효율적으로 처리하고 분석할 수 있는 첨단 컴퓨팅 하드웨어가 필요하며, 고성능 AI 솔루션에 대한 수요가 증가하고 있습니다.
• 산업 전반에 걸친 AI 도입 확대 : 헬스케어, 금융, 자동차 등 다양한 분야에서 AI가 광범위하게 도입됨에 따라 다양한 용도과 워크로드를 지원하는 전용 하드웨어의 필요성이 증가하고 있습니다.
• 하드웨어 혁신 : GPU 및 TPU와 같은 하드웨어 기술의 지속적인 발전은 성능과 효율성을 향상시키고 AI 컴퓨팅 솔루션의 채택을 촉진할 것입니다.
• 클라우드 컴퓨팅의 부상 : 클라우드 컴퓨팅 서비스의 성장은 대규모 클라우드 인프라를 지원하고 확장 가능한 솔루션을 제공할 수 있는 AI 하드웨어에 대한 수요를 창출하고 있습니다.

AI 컴퓨팅 하드웨어 시장이 해결해야 할 과제는 다음과 같습니다:

• 높은 개발 비용 : 최첨단 AI 컴퓨팅 하드웨어 개발에는 연구, 제조, 테스트 등 막대한 비용이 소요되어 신규 진입 장벽으로 작용할 수 있습니다.
• 빠른 기술 혁신 : 기술 발전 속도가 빠르기 때문에 지속적인 기술 혁신과 업데이트가 필요하며, 기업이 최신 개발에 대응하는 것이 과제입니다.
• 규제 대응 : GDPR(EU 개인정보보호규정)과 같은 데이터 프라이버시 및 보안 규정 준수는 AI 하드웨어의 설계 및 배포에 영향을 미칠 수 있으며, 시장 기업에게 도전이 될 수 있습니다.

이러한 촉진요인과 과제는 AI 컴퓨팅 하드웨어 시장을 형성하고 그 성장과 발전에 영향을 미칩니다. 이러한 요인을 해결하는 것은 기업이 AI 기술의 진화하는 환경에서 성공하고 번영하기 위해 매우 중요합니다.

목차

제1장 개요

제2장 세계의 AI 컴퓨팅 하드웨어 시장 : 시장 역학

• 서론, 배경, 분류
• 공급망
• 업계 촉진요인과 과제

제3장 시장 동향과 예측 분석(2019-2031년)

• 거시경제 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 AI 컴퓨팅 하드웨어 시장 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 AI 컴퓨팅 하드웨어 시장 : 유형별
  • 스탠드얼론 비전 프로세서
  • 임베디드 비전 프로세서
  • 스탠드얼론 사운드 프로세서
  • 임베디드 사운드 프로세서
• 세계의 AI 컴퓨팅 하드웨어 시장 : 용도별
  • BFSI
  • 자동차
  • 헬스케어
  • IT·통신
  • 항공우주·방위
  • 에너지·유틸리티
  • 정부·공공 서비스
  • 기타

제4장 지역별 시장 동향과 예측 분석(2019-2031년)

• 세계의 AI 컴퓨팅 하드웨어 시장 : 지역별
• 북미의 AI 컴퓨팅 하드웨어 시장
• 유럽의 AI 컴퓨팅 하드웨어 시장
• 아시아태평양의 AI 컴퓨팅 하드웨어 시장
• 기타 지역의 AI 컴퓨팅 하드웨어 시장

제5장 경쟁 분석

• 제품 포트폴리오 분석
• 운영 통합
• Porter's Five Forces 분석

제6장 성장 기회와 전략 분석

• 성장 기회 분석
  • 세계의 AI 컴퓨팅 하드웨어 시장의 성장 기회 : 유형별
  • 세계의 AI 컴퓨팅 하드웨어 시장의 성장 기회 : 용도별
  • 세계의 AI 컴퓨팅 하드웨어 시장의 성장 기회 : 지역별
• 세계의 AI 컴퓨팅 하드웨어 시장의 새로운 동향
• 전략 분석
  • 신제품 개발
  • 세계의 AI 컴퓨팅 하드웨어 시장의 능력 확대
  • 세계의 AI 컴퓨팅 하드웨어 시장에서의 합병, 인수, 합병사업
  • 인증과 라이선싱

제7장 주요 기업의 기업 개요

• Cadence Design Systems
• Synopsys
• NXP Semiconductors
• CEVA
• Allied Vision Technologies
• Arm Limited
• Knowles Electronics
• GreenWaves Technologies
• Andrea Electronics Corporation
• Basler

영문목차

The future of the global AI computing hardware market looks promising with opportunities in the BFSI, automotive, healthcare, IT & telecom, aerospace & defense, energy & utility, and government & public service markets. The global AI computing hardware market is expected to grow with a CAGR of 25.3% from 2025 to 2031. The major drivers for this market are increasing integration of AI in various industries for automation & efficiency, rising demand for data processing, and growing need for high-performance computing to manage big data and analytics.

• Lucintel forecasts that, within the type category stand-alone vision processor segment is expected to witness the highest growth over the forecast period.
• Within the application category, BFSI is expected to witness the highest growth.
• In terms of regions, APAC is expected to witness the highest growth over the forecast period.

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Emerging Trends in the AI Computing Hardware Market

The AI computing hardware market is experiencing several emerging trends driven by technological advancements and evolving industry needs. These trends are shaping the future of AI hardware and influencing how organizations deploy AI solutions.

• Rise of AI-specific processors: AI-specific processors, such as TPUs and FPGAs, are becoming more prevalent. These processors are designed to handle AI workloads more efficiently than general-purpose CPUs, improving performance and reducing energy consumption.
• Increased focus on energy efficiency: Energy-efficient AI hardware is gaining traction due to growing concerns about power consumption and sustainability. New designs are optimizing power usage while maintaining high performance, addressing the environmental impact of large-scale AI deployments.
• Advancements in quantum computing: Quantum computing is emerging as a potential game-changer for AI. While still in the experimental phase, advancements in quantum processors could revolutionize AI by solving complex problems faster than classical computers.
• Integration with edge computing: AI hardware is increasingly being integrated with edge computing to enable real-time data processing and analysis. This trend supports applications in IoT and smart devices, reducing latency and improving responsiveness.
• Development of modular and scalable solutions: Modular and scalable AI hardware solutions are being developed to cater to various needs, from small-scale applications to large-scale data centers. This flexibility allows organizations to easily upgrade and expand their AI infrastructure.

These emerging trends are reshaping the AI computing hardware market by driving innovations in processing capabilities, energy efficiency, and integration with new technologies. As these trends evolve, they will significantly impact how AI solutions are developed and deployed across industries.

Recent Developments in the AI Computing Hardware Market

Recent developments in AI computing hardware reflect the rapid pace of innovation and the increasing demands of AI applications. These advancements are crucial for enhancing performance, efficiency, and capabilities in AI-driven technologies.

• Launch of next-generation GPUs: New GPUs with enhanced processing power and efficiency have been launched, supporting more complex AI models and faster training times. These GPUs are critical for advancing AI research and applications.
• Advancement of AI accelerators: AI accelerators, including TPUs and custom-designed chips, are being introduced to optimize AI workloads. These accelerators offer significant improvements in speed and energy efficiency for AI computations.
• Development of neuromorphic chips: Neuromorphic chips that mimic the human brain's architecture are being developed to improve AI's ability to process and learn from sensory data. This technology holds promise for more advanced and efficient AI systems.
• Integration of AI hardware with cloud platforms: AI hardware is increasingly being integrated with cloud platforms, providing scalable and flexible solutions for businesses. This integration allows for more efficient data processing and access to powerful computing resources.
• Advancements in cooling technologies: New cooling technologies are being developed to address the heat generated by high-performance AI hardware. Innovations in cooling solutions are crucial for maintaining hardware reliability and performance.

These key developments are driving significant progress in the AI computing hardware market by enhancing performance, efficiency, and scalability. They are essential for supporting the growing demands of AI applications and ensuring robust and reliable hardware solutions.

Strategic Growth Opportunities for AI Computing Hardware Market

The AI computing hardware market presents several strategic growth opportunities across various applications. Leveraging these opportunities can drive innovation and expansion in the AI hardware sector.

• Expansion into autonomous vehicles: AI computing hardware is increasingly being used in autonomous vehicles to process real-time data from sensors and cameras. This application is driving demand for high-performance and reliable hardware solutions in the automotive industry.
• Growth in data centers: The expansion of data centers to support AI workloads is creating opportunities for AI hardware providers. Increased demand for processing power and storage drives investments in advanced computing infrastructure.
• Development of AI-enabled healthcare solutions: AI computing hardware is playing a critical role in developing healthcare solutions, such as diagnostic tools and personalized medicine. Growth in this sector presents opportunities for specialized hardware tailored to medical applications.
• Advancements in smart cities: AI hardware is essential for smart city initiatives, including traffic management and public safety systems. The need for efficient and scalable computing solutions is driving growth in this application area.
• Integration with 5G networks: The rollout of 5G networks is creating opportunities for AI hardware that supports high-speed data processing and low-latency applications. Integration with 5G enhances the capabilities of AI solutions in various sectors.

These strategic growth opportunities highlight the diverse applications of AI computing hardware and underscore the potential for innovation and expansion in the market. Capitalizing on these opportunities will drive advancements and growth in the AI hardware industry.

AI Computing Hardware Market Driver and Challenges

The AI computing hardware market is influenced by various technological, economic, and regulatory factors. Understanding these drivers and challenges is essential for navigating the market and capitalizing on opportunities.

The factors responsible for driving the AI computing hardware market include:

• Advancements in AI Algorithms: Improved AI algorithms increase the demand for powerful computing hardware capable of handling complex computations, driving innovation and investments in AI hardware.
• Growing Data Volume: The exponential growth of data requires advanced computing hardware to process and analyze large datasets efficiently, fueling the demand for high-performance AI solutions.
• Increased Adoption of AI Across Industries: The widespread adoption of AI across sectors like healthcare, finance, and automotive drives the need for specialized hardware to support diverse applications and workloads.
• Technological Innovations in Hardware: Ongoing advancements in hardware technologies, such as GPUs and TPUs, enhance performance and efficiency, driving further adoption of AI computing solutions.
• Rise in Cloud Computing: The growth of cloud computing services creates demand for AI hardware capable of supporting large-scale cloud infrastructure and providing scalable solutions.

Challenges in the AI computing hardware market are:

• High development costs: The development of cutting-edge AI computing hardware involves significant costs, including research, production, and testing, which can be a barrier to entry for new players.
• Rapid technological changes: The fast pace of technological advancements requires continuous innovation and updates, posing challenges for companies to keep up with the latest developments.
• Regulatory compliance: Compliance with data privacy and security regulations, such as GDPR, can impact the design and deployment of AI hardware, posing challenges for market players.

These drivers and challenges shape the AI computing hardware market, influencing its growth and development. Addressing these factors is crucial for companies to succeed and thrive in the evolving landscape of AI technology.

List of AI Computing Hardware Companies

Companies in the market compete based on product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies AI computing hardware companies cater to increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the AI computing hardware companies profiled in this report include-

• Cadence Design Systems
• Synopsys
• NXP Semiconductors
• CEVA
• Allied Vision Technologies
• Arm Limited
• Knowles Electronics
• GreenWaves Technologies
• Andrea Electronics Corporation
• Basler

AI Computing Hardware by Segment

The study includes a forecast for the global AI computing hardware market by type, application, and region.

AI Computing Hardware Market by Type [Analysis by Value from 2019 to 2031]:

• Stand-alone Vision Processor
• Embedded Vision Processor
• Stand-alone Sound Processor
• Embedded Sound Processor

AI Computing Hardware Market by Application [Analysis by Value from 2019 to 2031]:

• BFSI
• Automotive
• Healthcare
• IT & Telecom
• Aerospace & Defense
• Energy & Utilities
• Government & Public Services
• Others

AI Computing Hardware Market by Region [Analysis by Value from 2019 to 2031]:

• In terms of regions, North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the AI Computing Hardware Market

Major players in the market are expanding their operations and forming strategic partnerships to strengthen their positions. Below are recent developments by major AI computing hardware producers in key regions: the US, China, India, Japan, and Germany.

• United States: The US has seen significant advancements in AI computing hardware, with major tech companies introducing next-generation GPUs and specialized AI chips. Developments include enhancements in processing power and energy efficiency, which are crucial for training large-scale AI models and supporting complex algorithms.
• China: China is focusing on developing its own AI computing hardware to reduce reliance on foreign technology. Recent innovations include advanced AI processors and accelerators designed to enhance performance in areas such as facial recognition and natural language processing, aligning with the country's strategic technological goals.
• Germany: In Germany, there is a strong emphasis on integrating AI computing hardware into industrial applications. Recent developments include high-performance computing (HPC) systems tailored for AI-driven research and manufacturing processes, aimed at boosting productivity and innovation in various sectors.
• India: India is witnessing growth in AI computing hardware with an emphasis on affordability and scalability. Recent developments include cost-effective AI accelerators and cloud-based solutions that support startups and SMEs in leveraging AI technologies for diverse applications, from healthcare to finance.
• Japan: Japan is advancing in AI computing hardware by focusing on energy-efficient solutions and integration with robotics. Recent developments include specialized AI chips designed for real-time data processing and robotics applications, enhancing automation and smart manufacturing capabilities.

Features of the Global AI Computing Hardware Market

Market Size Estimates: AI computing hardware market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: AI computing hardware market size by type, application, and region in terms of value ($B).

Regional Analysis: AI computing hardware market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the AI computing hardware market.

Strategic Analysis: This includes M&A, new product development, and the competitive landscape of the AI computing hardware market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers the following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the AI computing hardware market by type (stand-alone vision processor, embedded vision processor, stand-alone sound processor, and embedded sound processor), application (BFSI, automotive, healthcare, IT & telecom, aerospace & defense, energy & utilities, government & public services, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global AI Computing Hardware Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global AI Computing Hardware Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global AI Computing Hardware Market by Type
  • 3.3.1: Stand-alone Vision Processor
  • 3.3.2: Embedded Vision Processor
  • 3.3.3: Stand-alone Sound Processor
  • 3.3.4: Embedded Sound Processor
• 3.4: Global AI Computing Hardware Market by Application
  • 3.4.1: BFSI
  • 3.4.2: Automotive
  • 3.4.3: Healthcare
  • 3.4.4: IT & Telecom
  • 3.4.5: Aerospace & Defense
  • 3.4.6: Energy & Utilities
  • 3.4.7: Government & Public Services
  • 3.4.8: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global AI Computing Hardware Market by Region
• 4.2: North American AI Computing Hardware Market
  • 4.2.1: North American AI Computing Hardware Market by Type: Stand-alone Vision Processor, Embedded Vision Processor, Stand-alone Sound Processor, and Embedded Sound Processor
  • 4.2.2: North American AI Computing Hardware Market by Application: BFSI, Automotive, Healthcare, IT & Telecom, Aerospace & Defense, Energy & Utilities, Government & Public Services, and Others
• 4.3: European AI Computing Hardware Market
  • 4.3.1: European AI Computing Hardware Market by Type: Stand-alone Vision Processor, Embedded Vision Processor, Stand-alone Sound Processor, and Embedded Sound Processor
  • 4.3.2: European AI Computing Hardware Market by Application: BFSI, Automotive, Healthcare, IT & Telecom, Aerospace & Defense, Energy & Utilities, Government & Public Services, and Others
• 4.4: APAC AI Computing Hardware Market
  • 4.4.1: APAC AI Computing Hardware Market by Type: Stand-alone Vision Processor, Embedded Vision Processor, Stand-alone Sound Processor, and Embedded Sound Processor
  • 4.4.2: APAC AI Computing Hardware Market by Application: BFSI, Automotive, Healthcare, IT & Telecom, Aerospace & Defense, Energy & Utilities, Government & Public Services, and Others
• 4.5: ROW AI Computing Hardware Market
  • 4.5.1: ROW AI Computing Hardware Market by Type: Stand-alone Vision Processor, Embedded Vision Processor, Stand-alone Sound Processor, and Embedded Sound Processor
  • 4.5.2: ROW AI Computing Hardware Market by Application: BFSI, Automotive, Healthcare, IT & Telecom, Aerospace & Defense, Energy & Utilities, Government & Public Services, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global AI Computing Hardware Market by Type
  • 6.1.2: Growth Opportunities for the Global AI Computing Hardware Market by Application
  • 6.1.3: Growth Opportunities for the Global AI Computing Hardware Market by Region
• 6.2: Emerging Trends in the Global AI Computing Hardware Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global AI Computing Hardware Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global AI Computing Hardware Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Cadence Design Systems
• 7.2: Synopsys
• 7.3: NXP Semiconductors
• 7.4: CEVA
• 7.5: Allied Vision Technologies
• 7.6: Arm Limited
• 7.7: Knowles Electronics
• 7.8: GreenWaves Technologies
• 7.9: Andrea Electronics Corporation
• 7.10: Basler