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市场调查报告书
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1456929

深度学习处理器市场 - 2024 年至 2029 年预测

Deep Learning Processor Market - Forecasts from 2024 to 2029
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 142 Pages | 商品交期: 最快1-2个工作天内

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简介目录

深度学习处理器市场预计将从2022年的30.84亿美元成长到2029年的122.91亿美元，复合年增长率为21.83%。

深度学习是机器学习的子集，机器学习是人工智慧的另一个子集。由于巨量资料量的增加以及人工智慧和机器学习的日益普及等因素，深度学习处理器市场正在成长。各行各业都在利用人工智慧技术，这也推动了深度学习处理器市场的成长。目前，各种技术来源产生的资料量不断增加，对深度学习处理器执行更快、更进阶分析的需求不断增加。此外，各国对智慧家庭和智慧城市计划的投资增加也导致了深度学习处理器的快速采用，对市场成长产生了正面影响。为深度学习处理器市场提供成长潜力的其他因素包括增加对人工智慧新兴企业和智慧机器人研发的投资。

然而，技术纯熟劳工的缺乏正在限制深度学习处理器市场的成长。管理深度学习软体及其使用需要工作人员能够处理或执行用于人工智慧开发的复杂演算法。此外，管理人工智慧和自动化系统有时可能是一个挑战。为了充分利用深度学习，您需要出色的软体工程技能以及分散式程式设计、并发程式设计和通讯协定调试方面的丰富经验。

市场驱动因素：

  • 深度学习在各行业的采用率不断提高。

影响深度学习处理器市场的关键因素之一是深度学习在各个领域的使用不断增长。随着医疗保健、金融、製造和技术领域的公司采用深度学习技术来完成自主系统和医学影像分析等任务，对旨在有效管理深度神经网路运算复杂性的处理器的需求不断增长。这种需求的增加凸显了对高效能运算系统的需求，并促进了客製化，因为深度学习处理器在设计时考虑了各行业的特定应用。透过将深度学习融入边缘设备，进一步加速了支援智慧相机、物联网设备和其他边缘运算场景的即时处理器的开发。

  • 深度神经网路复杂性的增加预计将推动市场发展。

深度学习处理器市场受到深度神经网路日益复杂性的显着影响，影响市场动态和技术开拓。随着深度神经网路变得更加复杂以实现更高的精度并处理更困难的任务，对能够管理更大运算需求的处理器的需求也在增长。深度学习处理器凭藉其专业设计和并行处理能力，已成为有效管理训练和操作高级神经网路模型所需的复杂运算的重要组件。优化的性能和效率要求进一步推动了处理器设计创新，重点是降低延迟和提高能源效率。

  • 基于晶片的 GPU 预计将占据很大的市场份额。

从晶片类型来看，GPU（图形处理单元）占据了很大的份额。玩游戏和看影片变得越来越普及。然而，随着技术的进步，GPU 越来越多地用于高解析度影像和人工智慧 (AI)。低功耗技术的使用也推动了需求。它还包括专用积体电路 (ASIC)、微处理器单元 (CPU) 和现场可编程闸阵列 (FPGA)。由于量子运算系统的利用率不断提高，CPU 晶片领域在预测期内将以显着的复合年增长率成长。量子运算可以以最快的速度解决复杂的演算法，因此最近被大型跨国公司和IT公司广泛应用。这将对深度学习晶片市场的成长产生正面影响。 FPGA晶片市场不断增长是因为FPGA晶片加快了配置速度，随着技术每年的发展，客户需要跟上当前的趋势进行更新。为了根据行业要求执行特定任务，该公司正在使用 ASIC 晶片来获得更好的性能和效率。

从技术角度来看，系统晶片预计将占据很大的市场份额。

智慧型手机和平板电脑市场的不断扩大正在推动对系统晶片处理器的需求。晶片系统、将中央处理单元、记忆体、输入/输出埠、辅助记忆体等整合在单一硬币大小的基板或微晶片上，使其成为智慧型手机的理想选择。智慧型手机和平板电脑具有晶片系统，可实现更好的效能和更快的多工活动。由于 3D 开发的增加，系统级封装市场正在不断扩大。

从最终用户产业来看，消费电子产品预计将成为成长最快的领域之一。

深度学习处理器广泛应用于消费性电子产业。随着技术的进步，市场出现了需要具有改进应用的更好设备的市场。人工智慧和机器学习在这一领域的日益普及正在扩大深度学习处理器市场。该公司正在智慧型手机中使用机器学习晶片来改进其功能并最大化其功能，例如更快的处理器和增强的多任务处理能力。人工智慧应用程式越来越多地融入智慧型手机和平板电脑中，以改善使用者介面和客户体验，从而推动对深度学习处理器的需求。在医疗保健、通讯和技术等行业，新设备配备了先进技术，并且越来越多地使用深度学习处理器来更快、更有效率地工作。深度学习处理器在该行业中越来越多的应用是利用人工智慧和增强智慧来改善客户体验，这正在推动深度学习处理器的市场成长。

预计北美将成为主要的区域市场。

全球深度学习处理器市场分为五个地区：北美、南美、欧洲、中东/非洲、亚太地区。北美预计将成为最大的市场。这项优势主要归功于该地区主要市场参与企业的支持，较早采用了先进技术。美国增加研发投资以开发人工智慧的广泛应用也推动了该地区的市场成长。亚太和欧洲地区的深度学习处理器市场预计在未来五年内将出现显着的市场成长率。

主要进展：

  • 2022 年 5 月，英特尔公司发表了第二代 Habana AI，这是一款具有高效能和高效率的深度学习处理器。新晶片是 Habana Gaudi2 和 Habana Greco，均采用 7 奈米技术。
  • 2022 年 2 月，AlphaICs 宣布提供适用于 Vision A 的 Gluon 深度学习协处理器的工程样本。这款先进的边缘推理晶片使客户能够将 AI 功能添加到现有的基于 X86/ARM 的系统中，从而显着节省成本。该晶片具有市场上用于神经网路分类和检测的最高 fps/瓦性能。

目录

第一章 简介

  • 市场概况
  • 市场定义
  • 调查范围
  • 市场区隔
  • 货币
  • 先决条件
  • 基准年和预测年时间表
  • 相关利益者的主要利益

第二章调查方法

  • 研究设计
  • 调查过程

第三章执行摘要

  • 主要发现
  • CXO观点

第四章市场动态

  • 市场驱动因素
  • 市场限制因素
  • 波特五力分析
  • 产业价值链分析
  • 分析师观点

第五章深度学习处理器市场：按晶片类型

  • 介绍
  • GPU
  • ASIC
  • CPU
  • FPGA

第六章深度学习处理器市场：依技术分类

  • 介绍
  • 处理器系统 (SIC)
  • 系统级封装(SIP)
  • 多处理器模组
  • 其他的

第七章深度学习处理器市场：依行业

  • 介绍
  • 家用电器
  • 通讯科技
  • 零售
  • 医疗保健
  • 车
  • 其他的

第 8 章深度学习处理器市场：按地区

  • 介绍
  • 北美洲
  • 南美洲
  • 欧洲
  • 中东/非洲
  • 亚太地区

第九章竞争环境及分析

  • 主要企业及策略分析
  • 市场占有率分析
  • 合併、收购、协议和合作
  • 竞争对手仪表板

第十章 公司简介

  • ARM Limited
  • NVIDIA Corporation
  • Microsoft
  • Samsung
  • Qualcomm
  • Graphcore
  • Advanced Micro Devices
  • Adapteva
  • Intel Corporation
简介目录
Product Code: KSI061611686

The deep learning processor market is expected to grow at a CAGR of 21.83% from US$3.084 billion in 2022 to US$12.291 billion in 2029.

Deep learning is a subset of machine learning, which is another subset of artificial intelligence. The deep learning processors market is growing owing to factors such as the growing volume of big data along with the increasing popularity of artificial intelligence and machine learning. Various industries are using AI technology, which is also driving the market growth of deep learning processors. The increasing amount of data generated nowadays from all technical sources is growing the requirement for faster and more advanced deep learning processors for faster analysis. Increasing investments in smart homes and smart city projects in various countries will also lead to a surge in the adoption of deep learning processors, thus positively impacting the market growth. Other factors that offer growth potential for the deep learning processor market include rising investments in AI startups and R&D in smart robotics.

However, the lack of a skilled workforce is limiting the market growth of the deep learning processor market. A worker with the ability to process or carry out complex algorithms for AI development is required to manage deep learning software and its applications. Furthermore, managing AI and automated systems can be challenging at times. To get the most out of deep learning, exceptional software engineering skills and significant experience with distributed and concurrent programming, as well as debugging with communications protocols, are required.

MARKET DRIVERS:

  • Increased adoption of deep learning in various industries.

One major factor affecting the deep learning processor market is the growing use of deep learning across a range of sectors. There is an increasing need for processors designed to effectively manage the computational complexity of deep neural networks as companies in the healthcare, finance, manufacturing, and technology sectors adopt deep learning techniques for tasks like autonomous systems and medical image analysis. This increase in demand highlights the need for high-performance computing systems and encourages customization since deep learning processors are designed with particular applications in mind for different industries. The development of real-time processors, which support applications in smart cameras, IoT devices, and other edge computing scenarios, is further accelerated by the incorporation of deep learning into edge devices.

  • The growing complexity of the deep neural networks is predicted to propel the market.

The deep learning processors market is heavily impacted by the increasing intricacy of deep neural networks, which has an impact on both market dynamics and technological developments. There is a growing need for processors that can manage the greater computing demands as deep neural networks become more complicated to attain higher accuracy and tackle harder jobs. To effectively manage the complicated computations necessary in training and operating sophisticated neural network models, deep learning processors, which have specialized designs and parallel processing capabilities have become indispensable components. Innovation in processor design is further driven by the requirement for optimized performance and efficiency, with an emphasis on lowering latency and increasing energy efficiency.

  • Chip-type GPU is predicted to have a sizable share of the market.

GPU (graphics processing units) account for a significant market share by chip type. It's becoming more popular for gaming and video viewing. However, as technology advances, the GPU is increasingly being used for high-resolution images and artificial intelligence (AI). The use of low-power technology is also increasing demand. The deep learning processor segment also consists of application-specific integrated circuits (ASICs) microprocessor units (CPUs), and field-programmable gate arrays (FPGAs). The increasing use of the quantum computing system is making the CPU chip segment grow at a substantial CAGR during the forecast period. Quantum computing is highly used these days by big multinational and information technology companies owing to their ability to solve complex algorithms in the fastest time. This positively impacts the market growth of deep-learning chips. The FPGA chip market is growing as it makes configuration faster and with developing technology every year, customers need to update according to the current trend making them go for FPGA chips for faster change. To carry out specific tasks according to the requirements of the industry, companies are using ASIC chips for better performance and efficiency.

By technology, System-On-Chip is anticipated to hold a sizable share of the market.

The growing market for smartphones and tablets is increasing the demand for System-On-Chip processors in the market. A System-On-Chip includes a central processing unit, memory, input/output ports, and secondary storage - all on a single substrate or microchip, the size of a coin, which is perfectly suitable for smartphones. Smartphones and tablets are enabled with a System-on-chip to provide for better performance and faster processing of multi-task activities. The increasing use of 3D development is growing the market for System-In-package.

By end-user industry, Consumer Electronics is predicted to be one of the fastest growing segments.

A deep learning processor is widely used across the consumer electronics industry. The increasing advancement in technology is building the market for better devices with improved applications. The increasing use of artificial intelligence and machine learning, across this sector is growing the market for deep learning processors. Companies are using machine learning chips in smartphones to improve their features and maximize capabilities, like a faster processor and improved multi-tasking ability. Artificial intelligence applications are increasingly being embedded within smartphones and tablets to improve user interfaces and customer experiences, driving up demand for deep learning processors. New devices are coming with advanced technologies for industries like healthcare and communication & technology, which are heavily using deep learning processors for faster work and higher efficiency. The rising application of deep learning processors in this industry is to improve customer experience by using artificial intelligence and augmented reality, which, in turn, is fueling the market growth of deep learning processors.

North America is anticipated to be the major regional market.

The global deep learning processor market is divided into five regions, North America, South America, Europe, the Middle East and Africa, and the Asia Pacific. North America is anticipated to be the largest market. This dominance is majorly attributed to the early adoption of advanced technologies supported by the presence of major market players in the region. Rising investments in R&D to develop a wider range of applications of artificial intelligence in the U.S. are also bolstering market growth in this region. The APAC and European regional markets for deep learning processors are predicted to witness a significant market growth rate during the next five years.

Key Developments:

  • Intel Corp. introduced its second-generation Habana AI, deep learning processors, in May 2022, delivering high performance and efficiency. The new chips are the Habana Gaudi2 and Habana Greco, which use 7-nanometer technology. It provides customers with a wide range of solution options-from cloud to edge-to address the growing number and complexity of AI workloads.
  • In February 2022, AlphaICs announced the availability of engineering samples of the Gluon-Deep Learning Co-Processor' For Vision AI, an advanced edge inference chip that enables customers to add AI capability to existing X86 / ARM-based systems, resulting in significant cost savings. It has the best fps/watt performance for the classification and detection of Neural Networks in the market.

Segmentation:

By Chip Type

  • GPU
  • ASIC
  • CPU
  • FPGA

By Technology

  • System-On-Processor (SIC)
  • System-IN-Package (SIP)
  • Multi-Processor Module
  • Others

By Industry Vertical

  • Consumer Electronics
  • Communication & Technology
  • Retail
  • Healthcare
  • Automotive
  • Others

By Geography

  • North America
  • USA
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • Germany
  • France
  • United Kingdom
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • Israel
  • UAE
  • Others
  • Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Thailand
  • Taiwan
  • Indonesia
  • Others

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Overview
  • 1.2. Market Definition
  • 1.3. Scope of the Study
  • 1.4. Market Segmentation
  • 1.5. Currency
  • 1.6. Assumptions
  • 1.7. Base, and Forecast Years Timeline
  • 1.8. Key Benefits to the stakeholder

2. RESEARCH METHODOLOGY

  • 2.1. Research Design
  • 2.2. Research Processes

3. EXECUTIVE SUMMARY

  • 3.1. Key Findings
  • 3.2. CXO Perspective

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's Five Forces Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Power of Buyers
    • 4.3.3. Threat of New Entrants
    • 4.3.4. Threat of Substitutes
    • 4.3.5. Competitive Rivalry in the Industry
  • 4.4. Industry Value Chain Analysis
  • 4.5. Analyst View

5. DEEP LEARNING PROCESSOR MARKET, BY CHIP TYPE

  • 5.1. Introduction
  • 5.2. GPU
    • 5.2.1. Market Trends and Opportunities
    • 5.2.2. Growth Prospects
    • 5.2.3. Geographic Lucrativeness
  • 5.3. ASIC
    • 5.3.1. Market Trends and Opportunities
    • 5.3.2. Growth Prospects
    • 5.3.3. Geographic Lucrativeness
  • 5.4. CPU
    • 5.4.1. Market Trends and Opportunities
    • 5.4.2. Growth Prospects
    • 5.4.3. Geographic Lucrativeness
  • 5.5. FPGA
    • 5.5.1. Market Trends and Opportunities
    • 5.5.2. Growth Prospects
    • 5.5.3. Geographic Lucrativeness

6. DEEP LEARNING PROCESSOR MARKET, BY TECHNOLOGY

  • 6.1. Introduction
  • 6.2. System-on-Processor (SIC)
    • 6.2.1. Market Trends and Opportunities
    • 6.2.2. Growth Prospects
    • 6.2.3. Geographic Lucrativeness
  • 6.3. System-in-Package (SIP)
    • 6.3.1. Market Trends and Opportunities
    • 6.3.2. Growth Prospects
    • 6.3.3. Geographic Lucrativeness
  • 6.4. Multi-Processor Module
    • 6.4.1. Market Trends and Opportunities
    • 6.4.2. Growth Prospects
    • 6.4.3. Geographic Lucrativeness
  • 6.5. Others
    • 6.5.1. Market Trends and Opportunities
    • 6.5.2. Growth Prospects
    • 6.5.3. Geographic Lucrativeness

7. DEEP LEARNING PROCESSOR MARKET, BY INDUSTRY VERTICAL

  • 7.1. Introduction
  • 7.2. Consumer Electronics
    • 7.2.1. Market Trends and Opportunities
    • 7.2.2. Growth Prospects
    • 7.2.3. Geographic Lucrativeness
  • 7.3. Communication & Technology
    • 7.3.1. Market Trends and Opportunities
    • 7.3.2. Growth Prospects
    • 7.3.3. Geographic Lucrativeness
  • 7.4. Retail
    • 7.4.1. Market Trends and Opportunities
    • 7.4.2. Growth Prospects
    • 7.4.3. Geographic Lucrativeness
  • 7.5. Healthcare
    • 7.5.1. Market Trends and Opportunities
    • 7.5.2. Growth Prospects
    • 7.5.3. Geographic Lucrativeness
  • 7.6. Automotive
    • 7.6.1. Market Trends and Opportunities
    • 7.6.2. Growth Prospects
    • 7.6.3. Geographic Lucrativeness
  • 7.7. Others
    • 7.7.1. Market Trends and Opportunities
    • 7.7.2. Growth Prospects
    • 7.7.3. Geographic Lucrativeness

8. DEEP LEARNING PROCESSOR MARKET, BY GEOGRAPHY

  • 8.1. Introduction
  • 8.2. North America
    • 8.2.1. By Chip Type
    • 8.2.2. By Technology
    • 8.2.3. By Industry Vertical
    • 8.2.4. By Country
      • 8.2.4.1. USA
        • 8.2.4.1.1. Market Trends and Opportunities
        • 8.2.4.1.2. Growth Prospects
      • 8.2.4.2. Canada
        • 8.2.4.2.1. Market Trends and Opportunities
        • 8.2.4.2.2. Growth Prospects
      • 8.2.4.3. Mexico
        • 8.2.4.3.1. Market Trends and Opportunities
        • 8.2.4.3.2. Growth Prospects
  • 8.3. South America
    • 8.3.1. By Chip Type
    • 8.3.2. By Technology
    • 8.3.3. By Industry Vertical
    • 8.3.4. By Country
      • 8.3.4.1. Brazil
        • 8.3.4.1.1. Market Trends and Opportunities
        • 8.3.4.1.2. Growth Prospects
      • 8.3.4.2. Argentina
        • 8.3.4.2.1. Market Trends and Opportunities
        • 8.3.4.2.2. Growth Prospects
      • 8.3.4.3. Others
        • 8.3.4.3.1. Market Trends and Opportunities
        • 8.3.4.3.2. Growth Prospects
  • 8.4. Europe
    • 8.4.1. By Chip Type
    • 8.4.2. By Technology
    • 8.4.3. By Industry Vertical
    • 8.4.4. By Country
      • 8.4.4.1. Germany
        • 8.4.4.1.1. Market Trends and Opportunities
        • 8.4.4.1.2. Growth Prospects
      • 8.4.4.2. France
        • 8.4.4.2.1. Market Trends and Opportunities
        • 8.4.4.2.2. Growth Prospects
      • 8.4.4.3. United Kingdom
        • 8.4.4.3.1. Market Trends and Opportunities
        • 8.4.4.3.2. Growth Prospects
      • 8.4.4.4. Spain
        • 8.4.4.4.1. Market Trends and Opportunities
        • 8.4.4.4.2. Growth Prospects
      • 8.4.4.5. Others
        • 8.4.4.5.1. Market Trends and Opportunities
        • 8.4.4.5.2. Growth Prospects
  • 8.5. Middle East and Africa
    • 8.5.1. By Chip Type
    • 8.5.2. By Technology
    • 8.5.3. By Industry Vertical
    • 8.5.4. By Country
      • 8.5.4.1. Saudi Arabia
        • 8.5.4.1.1. Market Trends and Opportunities
        • 8.5.4.1.2. Growth Prospects
      • 8.5.4.2. UAE
        • 8.5.4.2.1. Market Trends and Opportunities
        • 8.5.4.2.2. Growth Prospects
      • 8.5.4.3. Israel
        • 8.5.4.3.1. Market Trends and Opportunities
        • 8.5.4.3.2. Growth Prospects
      • 8.5.4.4. Others
        • 8.5.4.4.1. Market Trends and Opportunities
        • 8.5.4.4.2. Growth Prospects
  • 8.6. Asia Pacific
    • 8.6.1. By Chip Type
    • 8.6.2. By Technology
    • 8.6.3. By Industry Vertical
    • 8.6.4. By Country
      • 8.6.4.1. China
        • 8.6.4.1.1. Market Trends and Opportunities
        • 8.6.4.1.2. Growth Prospects
      • 8.6.4.2. Japan
        • 8.6.4.2.1. Market Trends and Opportunities
        • 8.6.4.2.2. Growth Prospects
      • 8.6.4.3. South Korea
        • 8.6.4.3.1. Market Trends and Opportunities
        • 8.6.4.3.2. Growth Prospects
      • 8.6.4.4. India
        • 8.6.4.4.1. Market Trends and Opportunities
        • 8.6.4.4.2. Growth Prospects
      • 8.6.4.5. Thailand
        • 8.6.4.5.1. Market Trends and Opportunities
        • 8.6.4.5.2. Growth Prospects
      • 8.6.4.6. Indonesia
        • 8.6.4.6.1. Market Trends and Opportunities
        • 8.6.4.6.2. Growth Prospects
      • 8.6.4.7. Taiwan
        • 8.6.4.7.1. Market Trends and Opportunities
        • 8.6.4.7.2. Growth Prospects
      • 8.6.4.8. Others
        • 8.6.4.8.1. Market Trends and Opportunities
        • 8.6.4.8.2. Growth Prospects

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 9.1. Major Players and Strategy Analysis
  • 9.2. Market Share Analysis
  • 9.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 9.4. Competitive Dashboard

10. COMPANY PROFILES

  • 10.1. ARM Limited
  • 10.2. NVIDIA Corporation
  • 10.3. Microsoft
  • 10.4. Samsung
  • 10.5. Qualcomm
  • 10.6. Graphcore
  • 10.7. Advanced Micro Devices
  • 10.8. Adapteva
  • 10.9. Intel Corporation