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市场调查报告书
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1680220

人工智慧运算硬体市场报告：2031 年趋势、预测与竞争分析

AI Computing Hardware Market Report: Trends, Forecast and Competitive Analysis to 2031
出版日期: | 出版商: Lucintel | 英文 150 Pages | 商品交期: 3个工作天内

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简介目录

全球人工智慧运算硬体市场前景光明，在 BFSI、汽车、医疗保健、IT 和通讯、航太和国防、能源和公共以及政府和公共服务市场都机会。预计全球人工智慧运算硬体市场在 2025 年至 2031 年期间的复合年增长率将达到 25.3%。该市场的主要驱动力是各行业对人工智慧的整合程度不断提高，以实现自动化和效率，对资料处理的需求不断增长，以及对管理巨量资料和分析的高效能运算的需求不断增长。

  • Lucintel 表示，根据类型，独立视觉处理器将在预测期内经历最高的成长。
  • 根据应用，BFSI 预计将实现最高成长。
  • 根据地区来看，预计亚太地区将在预测期内实现最高成长。

AI运算硬体市场的策略性成长机会

人工智慧计算硬体市场为一系列应用提供了多种策略成长机会。利用这些机会可以帮助推动AI硬体领域的创新和扩展。

  • 扩展到自动驾驶汽车：人工智慧运算硬体越来越多地被用于自动驾驶汽车，以处理来自感测器和摄影机的即时资料。该应用使得汽车产业对高性能、可靠的硬体解决方案的需求日益增加。
  • 资料中心成长：资料中心的扩展以支援人工智慧工作负载为人工智慧硬体供应商创造了机会。对处理能力和储存的不断增长的需求正在推动对先进运算基础设施的投资。
  • 开发支援人工智慧的医疗保健解决方案：人工智慧运算硬体在开发诊断工具和个人化医疗等医疗保健解决方案中发挥关键作用。该领域的发展为开发医疗应用专用硬体提供了机会。
  • 智慧城市的进步：人工智慧硬体对于交通管理和公共系统等智慧城市计画至关重要。对高效、可扩展的运算解决方案的需求正在推动这一应用领域的成长。
  • 与5G网路融合：5G网路的推出为AI硬体支援高速资料处理和低延迟应用创造了机会。与5G的结合将增强各领域的AI解决方案的能力。

这些策略成长机会凸显了人工智慧运算硬体的多样化应用，并强调了市场创新和扩张的潜力。利用这些机会将推动人工智慧硬体产业的进步和成长。

人工智慧运算硬体市场驱动因素与挑战

AI计算硬体市场受到多种技术、经济和监管因素的影响。了解这些市场驱动因素和挑战对于驾驭市场和抓住机会至关重要。

AI计算硬体市场受到以下因素的推动：

  • 人工智慧演算法的进步：人工智慧演算法的日益复杂将推动对能够处理复杂运算的强大运算硬体的需求，从而刺激人工智慧硬体的创新和投资。
  • 资料量不断增加：资料的急剧增长需要先进的运算硬体来高效处理和分析大型资料集，从而推动对高效能人工智慧解决方案的需求。
  • 各行各业对人工智慧的应用日益广泛：医疗保健、金融和汽车等领域对人工智慧的广泛应用正在推动对专用硬体的需求，以支援多样化的应用程式和工作负载。
  • 硬体创新：GPU、TPU等硬体技术的持续进步将提高效能和效率，促进AI运算解决方案的进一步应用。
  • 云端运算的兴起：云端运算服务的成长正在创造对能够支援大规模云端基础设施并提供可扩展解决方案的人工智慧硬体的需求。

AI计算硬体市场面临的挑战有：

  • 开发成本高：开发尖端人工智慧运算硬体需要大量的研究、製造和测试成本，这可能会成为新公司的进入障碍。
  • 快速的技术创新：技术进步的快速步伐需要不断创新和更新，这对企业来说是个挑战，以跟上最新的发展。
  • 应对法规：遵守 GDPR 等资料隐私和安全法规可能会影响 AI 硬体的设计和部署，从而给市场参与者带来挑战。

这些驱动因素和挑战将塑造AI计算硬体市场并影响其成长和发展。解决这些因素对于企业在不断发展的人工智慧技术领域取得成功和发展至关重要。

目录

第一章执行摘要

第二章 全球人工智慧运算硬体市场：市场动态

  • 简介、背景和分类
  • 供应链
  • 产业驱动力与挑战

第三章市场趋势与预测分析（2019-2031）

  • 宏观经济趋势（2019-2024）及预测（2025-2031）
  • 全球人工智慧计算硬体市场趋势（2019-2024）及预测（2025-2031）
  • 全球人工智慧运算硬体市场（按类型）
    • 独立视觉处理器
    • 嵌入式视觉处理器
    • 独立声音处理器
    • 内建声音处理器
  • 全球人工智慧计算硬体市场（按应用）
    • BFSI
    • 车
    • 卫生保健
    • 资讯科技/通讯
    • 航太与国防
    • 能源与公共产业
    • 政府和公共服务
    • 其他的

第四章区域市场趋势与预测分析（2019-2031）

  • 全球人工智慧运算硬体市场（按地区）
  • 北美人工智慧运算硬体市场
  • 欧洲人工智慧运算硬体市场
  • 亚太地区人工智慧运算硬体市场
  • 世界其他地区人工智慧运算硬体市场

第五章 竞争分析

  • 产品系列分析
  • 营运整合
  • 波特五力分析

第六章 成长机会与策略分析

  • 成长机会分析
    • 全球人工智慧运算硬体市场成长机会（按类型）
    • 全球人工智慧计算硬体市场的成长机会（按应用）
    • 全球人工智慧运算硬体市场各区域成长机会
  • 全球人工智慧运算硬体市场的新趋势
  • 战略分析
    • 新产品开发
    • 全球人工智慧运算硬体市场容量不断扩大
    • 全球人工智慧运算硬体市场的併购和合资企业
    • 认证和许可

第七章主要企业简介

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

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.

Gain valuable insights for your business decisions with our comprehensive 150+ page report.

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