查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1456929

深度學習處理器市場 - 2024 年至 2029 年預測

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個工作天內

價格
簡介目錄

深度學習處理器市場預計將從2022年的30.84億美元成長到2029年的122.91億美元，複合年成長率為21.83%。

深度學習是機器學習的子集，機器學習是人工智慧的另一個子集。由於巨量資料量的增加以及人工智慧和機器學習的日益普及等因素，深度學習處理器市場正在成長。各行各業都在利用人工智慧技術，這也推動了深度學習處理器市場的成長。目前，各種技術來源產生的資料量不斷增加，對深度學習處理器執行更快、更進階分析的需求不斷增加。此外，各國對智慧家庭和智慧城市計劃的投資增加也導致了深度學習處理器的快速採用，對市場成長產生了正面影響。為深度學習處理器市場提供成長潛力的其他因素包括增加對人工智慧新興企業和智慧機器人研發的投資。

然而，技術純熟勞工的缺乏正在限制深度學習處理器市場的成長。管理深度學習軟體及其使用需要工作人員能夠處理或執行用於人工智慧開發的複雜演算法。此外，管理人工智慧和自動化系統有時可能是一個挑戰。為了充分利用深度學習，您需要出色的軟體工程技能以及分散式程式設計、並發程式設計和通訊協定調試方面的豐富經驗。

市場促進因素：

  • 深度學習在各行業的採用率不斷提高。

影響深度學習處理器市場的關鍵因素之一是深度學習在各個領域的使用不斷成長。隨著醫療保健、金融、製造和技術領域的公司採用深度學習技術來完成自主系統和醫學影像分析等任務，對旨在有效管理深度神經網路運算複雜性的處理器的需求不斷成長。這種需求的增加凸顯了對高效能運算系統的需求，並促進了客製化，因為深度學習處理器在設計時考慮了各行業的特定應用。透過將深度學習融入邊緣設備，進一步加速了支援智慧相機、物聯網設備和其他邊緣運算場景的即時處理器的開發。

  • 深度神經網路複雜性的增加預計將推動市場發展。

深度學習處理器市場受到深度神經網路日益複雜性的顯著影響，影響市場動態和技術開拓。隨著深度神經網路變得更加複雜以實現更高的精度並處理更困難的任務，對能夠管理更大運算需求的處理器的需求也在成長。深度學習處理器憑藉其專業設計和並行處理能力，已成為有效管理訓練和操作高級神經網路模型所需的複雜運算的重要組件。最佳化的性能和效率要求進一步推動了處理器設計創新，重點是降低延遲和提高能源效率。

  • 基於晶片的 GPU 預計將佔據很大的市場佔有率。

從晶片類型來看，GPU（圖形處理單元）佔據了很大的佔有率。玩遊戲和看影片變得越來越普及。然而，隨著技術的進步，GPU 擴大用於高解析度影像和人工智慧 (AI)。低功耗技術的使用也推動了需求。它還包括專用積體電路 (ASIC)、微處理器單元 (CPU) 和現場可編程閘陣列 (FPGA)。由於量子運算系統的利用率不斷提高，CPU 晶片領域在預測期內將以顯著的複合年成長率成長。量子運算可以以最快的速度解決複雜的演算法，因此最近被大型跨國公司和IT公司廣泛應用。這將對深度學習晶片市場的成長產生正面影響。 FPGA晶片市場不斷成長是因為FPGA晶片加快了配置速度，隨著技術每年的發展，客戶需要跟上當前的趨勢進行更新。為了根據行業要求執行特定任務，該公司正在使用 ASIC 晶片來獲得更好的性能和效率。

從技術角度來看，系統晶片預計將佔據很大的市場佔有率。

智慧型手機和平板電腦市場的不斷擴大正在推動對系統晶片處理器的需求。晶片系統、將中央處理單元、記憶體、輸入/輸出埠、輔助記憶體等整合在單一硬幣大小的基板或微晶片上，使其成為智慧型手機的理想選擇。智慧型手機和平板電腦具有晶片系統，可實現更好的效能和更快的多工活動。由於 3D 開發的增加，系統級封裝市場正在不斷擴大。

從最終用戶產業來看，消費電子產品預計將成為成長最快的領域之一。

深度學習處理器廣泛應用於消費性電子產業。隨著技術的進步，市場出現了需要具有改進應用的更好設備的市場。人工智慧和機器學習在這一領域的日益普及正在擴大深度學習處理器市場。該公司正在智慧型手機中使用機器學習晶片來改進其功能並最大化其功能，例如更快的處理器和增強的多任務處理能力。人工智慧應用程式擴大融入智慧型手機和平板電腦中，以改善使用者介面和客戶體驗，從而推動對深度學習處理器的需求。在醫療保健、通訊和技術等行業，新設備配備了先進技術，並且擴大使用深度學習處理器來更快、更有效率地工作。深度學習處理器在該行業中越來越多的應用是利用人工智慧和增強智慧來改善客戶體驗，這正在推動深度學習處理器的市場成長。

預計北美將成為主要的區域市場。

全球深度學習處理器市場分為五個地區：北美、南美、歐洲、中東/非洲、亞太地區。北美預計將成為最大的市場。這項優勢主要歸功於該地區主要市場參與企業的支持，較早採用了先進技術。美國增加研發投資以開發人工智慧的廣泛應用也推動了該地區的市場成長。亞太和歐洲地區的深度學習處理器市場預計在未來五年內將出現顯著的市場成長率。

主要進展：

  • 2022 年 5 月，英特爾公司發表了第二代 Habana AI，這是一款具有高效能和高效率的深度學習處理器。新晶片是 Habana Gaudi2 和 Habana Greco，均採用 7 奈米技術。
  • 2022 年 2 月，AlphaICs 宣布提供適用於 Vision A 的 Gluon 深度學習協處理器的工程樣本。這款先進的邊緣推理晶片使客戶能夠將 AI 功能添加到現有的基於 X86/ARM 的系統中，從而顯著節省成本。該晶片具有市場上用於神經網路分類和檢測的最高 fps/瓦性能。

目錄

第1章 簡介

  • 市場概況
  • 市場定義
  • 調查範圍
  • 市場區隔
  • 貨幣
  • 先決條件
  • 基準年和預測年時間表
  • 相關利益者的主要利益

第2章調查方法

  • 研究設計
  • 調查過程

第3章執行摘要

  • 主要發現
  • CXO觀點

第4章市場動態

  • 市場促進因素
  • 市場限制因素
  • 波特五力分析
  • 產業價值鏈分析
  • 分析師觀點

第5章深度學習處理器市場：按晶片類型

  • 介紹
  • GPU
  • ASIC
  • CPU
  • FPGA

第6章深度學習處理器市場：依技術分類

  • 介紹
  • 處理器系統 (SIC)
  • 系統級封裝(SIP)
  • 多處理器模組
  • 其他

第7章深度學習處理器市場：依行業

  • 介紹
  • 家用電器
  • 通訊科技
  • 零售
  • 醫療保健
  • 車
  • 其他

第 8 章深度學習處理器市場：按地區

  • 介紹
  • 北美洲
  • 南美洲
  • 歐洲
  • 中東/非洲
  • 亞太地區

第9章競爭環境及分析

  • 主要企業及策略分析
  • 市場佔有率分析
  • 合併、收購、協議和合作
  • 競爭對手儀表板

第10章 公司簡介

  • ARM Limited
  • NVIDIA Corporation
  • Microsoft
  • Samsung
  • Qualcomm
  • Graphcore
  • Advanced Micro Devices
  • Adapteva
  • Intel Corporation
簡介目錄
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