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人工智慧運算硬體市場報告：2031 年趨勢、預測與競爭分析

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個工作天內

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簡介目錄

全球人工智慧運算硬體市場前景光明，在 BFSI、汽車、醫療保健、IT 和通訊、航太和國防、能源和公共以及政府和公共服務市場都機會。預計全球人工智慧運算硬體市場在 2025 年至 2031 年期間的複合年成長率將達到 25.3%。該市場的主要驅動力是各行業對人工智慧的整合程度不斷提高，以實現自動化和效率，對資料處理的需求不斷成長，以及對管理巨量資料和分析的高效能運算的需求不斷成長。

  • Lucintel 表示，根據類型，獨立視覺處理器將在預測期內經歷最高的成長。
  • 根據應用，BFSI 預計將實現最高成長。
  • 根據地區來看，預計亞太地區將在預測期內實現最高成長。

AI運算硬體市場的策略性成長機會

人工智慧計算硬體市場為一系列應用提供了多種策略成長機會。利用這些機會可以幫助推動AI硬體領域的創新和擴展。

  • 擴展到自動駕駛汽車：人工智慧運算硬體擴大被用於自動駕駛汽車，以處理來自感測器和攝影機的即時資料。該應用使得汽車產業對高性能、可靠的硬體解決方案的需求日益增加。
  • 資料中心成長：資料中心的擴展以支援人工智慧工作負載為人工智慧硬體供應商創造了機會。對處理能力和儲存的不斷成長的需求正在推動對先進運算基礎設施的投資。
  • 開發支援人工智慧的醫療保健解決方案：人工智慧運算硬體在開發診斷工具和個人化醫療等醫療保健解決方案中發揮關鍵作用。該領域的發展為開發醫療應用專用硬體提供了機會。
  • 智慧城市的進步：人工智慧硬體對於交通管理和公共系統等智慧城市計畫至關重要。對高效、可擴展的運算解決方案的需求正在推動這一應用領域的成長。
  • 與5G網路融合：5G網路的推出為AI硬體支援高速資料處理和低延遲應用創造了機會。與5G的結合將增強各領域的AI解決方案的能力。

這些策略成長機會凸顯了人工智慧運算硬體的多樣化應用，並強調了市場創新和擴張的潛力。利用這些機會將推動人工智慧硬體產業的進步和成長。

人工智慧運算硬體市場促進因素與挑戰

AI計算硬體市場受到多種技術、經濟和監管因素的影響。了解這些市場促進因素和挑戰對於駕馭市場和抓住機會至關重要。

AI計算硬體市場受到以下因素的推動：

  • 人工智慧演算法的進步：人工智慧演算法的日益複雜將推動對能夠處理複雜運算的強大運算硬體的需求，從而刺激人工智慧硬體的創新和投資。
  • 資料量不斷增加：資料的急劇成長需要先進的運算硬體來高效處理和分析大型資料集，從而推動對高效能人工智慧解決方案的需求。
  • 各行各業對人工智慧的應用日益廣泛：醫療保健、金融和汽車等領域對人工智慧的廣泛應用正在推動對專用硬體的需求，以支援多樣化的應用程式和工作負載。
  • 硬體創新：GPU、TPU等硬體技術的持續進步將提高效能和效率，促進AI運算解決方案的進一步應用。
  • 雲端運算的興起：雲端運算服務的成長正在創造對能夠支援大規模雲端基礎設施並提供可擴展解決方案的人工智慧硬體的需求。

AI計算硬體市場面臨的挑戰有：

  • 開發成本高：開發尖端人工智慧運算硬體需要大量的研究、製造和測試成本，這可能會成為新公司的進入障礙。
  • 快速的技術創新：技術進步的快速步伐需要不斷創新和更新，這對企業來說是個挑戰，以跟上最新的發展。
  • 應對法規：遵守 GDPR 等資料隱私和安全法規可能會影響 AI 硬體的設計和部署，從而給市場參與者帶來挑戰。

這些促進因素和挑戰將塑造AI計算硬體市場並影響其成長和發展。解決這些因素對於企業在不斷發展的人工智慧技術領域取得成功和發展至關重要。

目錄

第1章執行摘要

第2章 全球人工智慧運算硬體市場：市場動態

  • 簡介、背景和分類
  • 供應鏈
  • 產業驅動力與挑戰

第3章市場趨勢與預測分析（2019-2031）

  • 宏觀經濟趨勢（2019-2024）及預測（2025-2031）
  • 全球人工智慧計算硬體市場趨勢（2019-2024）及預測（2025-2031）
  • 全球人工智慧運算硬體市場（按類型）
    • 獨立視覺處理器
    • 嵌入式視覺處理器
    • 獨立聲音處理器
    • 內建聲音處理器
  • 全球人工智慧計算硬體市場（按應用）
    • BFSI
    • 車
    • 衛生保健
    • 資訊科技/通訊
    • 航太與國防
    • 能源與公共產業
    • 政府和公共服務
    • 其他

第4章區域市場趨勢與預測分析（2019-2031）

  • 全球人工智慧運算硬體市場（按地區）
  • 北美人工智慧運算硬體市場
  • 歐洲人工智慧運算硬體市場
  • 亞太地區人工智慧運算硬體市場
  • 世界其他地區人工智慧運算硬體市場

第5章 競爭分析

  • 產品系列分析
  • 營運整合
  • 波特五力分析

第6章 成長機會與策略分析

  • 成長機會分析
    • 全球人工智慧運算硬體市場成長機會（按類型）
    • 全球人工智慧計算硬體市場的成長機會（按應用）
    • 全球人工智慧運算硬體市場各區域成長機會
  • 全球人工智慧運算硬體市場的新趨勢
  • 戰略分析
    • 新產品開發
    • 全球人工智慧運算硬體市場容量不斷擴大
    • 全球人工智慧運算硬體市場的併購和合資企業
    • 認證和許可

第7章主要企業簡介

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

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