딥러닝 시장 보고서 : 제품 유형, 용도, 최종 용도 산업, 아키텍처, 지역별(2024-2032년)

The global deep learning market size reached US$ 23.5 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 295.1 Billion by 2032, exhibiting a growth rate (CAGR) of 31.5% during 2024-2032. The increasing artificial intelligence (AI) adoption, advancements in data processing, the growing demand for image and speech recognition, investments in research and development (R&D), and the introduction of big data and cloud computing technologies are some of the major factors propelling the market.

Deep learning is a subfield of artificial intelligence (AI) that involves training artificial neural networks to learn and make decisions from vast amounts of data. These neural networks consist of interconnected layers of nodes, mimicking the structure of the human brain, the networks iteratively adjust their internal parameters to identify patterns, features, and representations within the data, allowing them to recognize objects, comprehend speech, translate languages, and even play strategic games. It also transforms various domains, including computer vision, natural language processing (NLP), and robotics, achieving remarkable breakthroughs in tasks previously considered challenging for traditional machine learning approaches.

The market is primarily driven by the significant expansion of the information technology (IT) industry. In addition, the growing trend of digitalization, and the widespread adoption of deep learning for automatically extracting raw data, making it a powerful tool for solving complex real-world problems with high accuracy and efficiency, is influencing market growth. It also processes data by automatically analyzing available data, resulting in more efficient and accurate decision-making. Moreover, the extensive service use of in cybersecurity, fraud detection, medical image analysis, and virtual patient assistance in healthcare represents another major growth-inducing factor. Besides this, the integration of big data analytics and cloud computing and ongoing research and development (R&D) efforts to improve hardware and software processing are further accelerating the market growth. Furthermore, the scalability and computational power offered by these technologies allow organizations to process and analyze vast datasets efficiently, thus creating a positive market outlook.

Deep Learning Market Trends/Drivers:

The rising demand for deep learning for image and speech recognition

The growing demand to analyze and identify patterns, objects, and features within images is escalating the market growth. Additionally, deep learning-powered medical imaging systems assist in diagnosing diseases, detecting anomalies, and assisting in surgical planning in the healthcare sector thus influencing the market growth. Moreover, in autonomous vehicles image recognition enables real-time identification of traffic signs, pedestrians, and obstacles, enhancing the safety and efficiency of self-driving cars, representing another major growth-inducing factor. Besides this, speech recognition is essential in the development of natural language processing (NLP) applications and voice assistants. Also, deep learning models are employed to transcribe speech into text, enabling voice-controlled virtual assistants including Siri, Alexa, and Google Assistant to understand and respond to user commands accurately. This has transformed the way people interact with technology and enabled hands-free and intuitive user experiences. Furthermore, the product adoption of for speech recognition in customer service centers, call centers, and language translation services is streamlining communication and improving response times thus propelling the market growth.

The increasing investment in research and development (R&D)

Deep learning continues to evolve rapidly, and organizations across industries are allocating substantial resources to enhance the capabilities and applications of this cutting-edge technology. Additionally, the investments in R&D focus on various aspects of learning and the development of novel algorithms and architectures that improve performance, accuracy, and efficiency, thus influencing market growth. Also, researchers are continuously exploring innovative techniques such as attention mechanisms, transformers, and generative adversarial networks (GANs) to achieve breakthroughs in natural language processing, computer vision, and other AI-driven tasks. Moreover, hardware optimization is another focal point of R&D investments. Organizations are developing specialized processors, such as graphical processing units (GPUs) and tensor processing units (TPUs), designed to accelerate deep learning computations. These hardware advancements enable faster training times and inference, making the models more accessible and scalable for businesses.

The implementation of favorable government initiatives

Government support and initiatives are essential in fostering the market growth. Additionally, governments are recognizing the transformative potential of artificial intelligence (AI), and actively investing AI research and development projects, and promoting research, development, thus influencing market growth. Moreover, financial investments from government agencies allow universities, research institutions, and private companies to undertake ambitious deep-learning projects that push the boundaries of innovation and drive technological advancements representing another major growth-inducing factor. Besides this, governments often establish AI-focused centers of excellence and innovation hubs that serve as collaborative spaces for researchers, academics, and industry experts which facilitate knowledge sharing, networking, and interdisciplinary research, fostering an environment conducive to breakthrough discoveries in deep learning. Furthermore, governments actively engage in public-private partnerships to accelerate the product adoption across industries and create policies and regulations that encourage responsible AI development and deployment thus propelling the market growth.

Deep Learning Industry Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global deep learning market report, along with forecasts at the global, regional and country levels from 2024-2032. Our report has categorized the market based on product type, application, end-use industry and architecture.

Breakup by Product Type:

Software

Services

Hardware

Software represents the most popular product type

The report has provided a detailed breakup and analysis of the market based on the product type. This includes software, services, and hardware. According to the report, software accounted for the largest market share.

Software is essential in the development and implementation of deep learning algorithms and models. It provides the necessary tools and frameworks for researchers, data scientists, and developers to create and train complex neural networks efficiently. As a result, software solutions have become indispensable for unlocking the full potential of technology. Moreover, the flexibility and scalability offered by the software make it highly attractive to businesses across various industries. Software-based solutions allow organizations to integrate deep learning capabilities into their existing systems and applications seamlessly empowering businesses to use the power of AI-driven insights and automation to optimize processes, improve decision-making, and enhance customer experiences.

Besides this, the open-source nature of many software platforms fosters collaboration and knowledge sharing within the AI community. Popular open-source libraries such as TensorFlow and PyTorch are essential in democratizing access to technology, enabling widespread adoption and innovation. Furthermore, the continuous advancements in software, driven by ongoing research and development, are resulting in improved performance and efficiency.

Breakup by Application:

Image Recognition

Signal Recognition

Data Mining

Others

Image recognition represents the most popular application segment

The report has provided a detailed breakup and analysis of the market based on the application. This includes image recognition, signal recognition, data mining, and others. According to the report, image recognition accounted for the largest market share.

Image recognition is currently dominating the market growth due to its wide-ranging applications and transformative impact across various industries. They are demonstrating exceptional capabilities in accurately identifying and analyzing objects, patterns, and features within images, making them highly sought after for diverse use cases. Moreover, deep learning-powered medical imaging systems aid in the early detection of diseases, assist in precise diagnoses, and support treatment planning in the healthcare industry.

Besides this, in the automotive sector, image recognition is essential for enabling advanced driver assistance systems (ADAS) and autonomous vehicles, enhancing safety and efficiency on the roads, thus accelerating the market growth. Moreover, the retail and e-commerce sectors use image recognition for visual search, product recommendation, and inventory management that enhances customer experiences, streamlines operations, and drives sales.

Breakup by End-Use Industry:

Security

Manufacturing

Retail

Automotive

Healthcare

Agriculture

Others

Security holds the largest share of the market

A detailed breakup and analysis of the market based on the end use industry has also been provided in the report. This includes security, manufacturing, retail, automotive, healthcare, agriculture, and others. According to the report, security accounted for the largest market share.

Deep learning technology offers unprecedented capabilities in detecting, analyzing, and responding to complex security breaches and attacks. In addition, the increasing demand for robust and advanced solutions to combat the ever-evolving landscape of cyber threats, is influencing the market growth. In the cybersecurity domain, deep learning algorithms excel in anomaly detection, identifying suspicious patterns and activities that traditional security systems may miss.

Moreover, the growing demand for cutting-edge security measures, such as deep learning-powered intrusion detection systems, malware detection, and behavioral analytics to offer organizations with enhanced defense mechanisms against emerging threats represents another major growth-inducing factor. Additionally, the vast amounts of data generated in the cybersecurity landscape require advanced data processing and analysis capabilities. It excels in handling big data and efficiently extracting meaningful insights, enabling security teams to make informed decisions and respond proactively to potential threats.

Breakup by Architecture:

RNN

CNN

DBN

DSN

GRU

A detailed breakup and analysis of the market based on the architecture has also been provided in the report. This includes RNN, CNN, DBN, DSN, and GRU.

Recurrent neural networks (RNN) are designed to handle sequential data, such as time series or natural language. Their recurrent nature allows them to capture temporal dependencies within the data. RNNs have internal memory that enables them to process sequences of variable length, making them ideal for tasks such as language modeling, machine translation, and sentiment analysis.

Moreover, convolutional neural networks (CNN) are employed for image and video processing tasks as they excel at feature extraction through convolutional layers, which scan input data with small filters to identify patterns and spatial relationships. CNNs are widely employed in image recognition, object detection, and image classification tasks due to their ability to automatically learn relevant visual features. Besides this, deep belief networks (DBN) are generative models that consist of multiple layers of stochastic, latent variables, used in unsupervised learning tasks, such as feature learning and dimensionality reduction, making them useful in applications such as speech recognition and recommendation systems.

Apart from this, deep stacking networks (DSN) are a type of autoencoder-based architecture used for unsupervised feature learning involving multiple stacked layers that progressively learn to encode and decode data representations which find applications in anomaly detection, data compression, and denoising tasks. Furthermore, gated recurrent units (GRU) are a variant of RNNs that aim to address the vanishing gradient problem and improve training efficiency which use gating mechanisms to regulate the flow of information through the network, allowing them to retain essential information for longer sequences and avoid long-term dependencies issues.

Breakup by Region:

North America

United States

Canada

Asia Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America exhibits a clear dominance in the market

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America is home to some of the world's leading tech giants, research institutions, and AI startups, which heavily invest in research and development (R&D) for advanced technology. The presence of these industry leaders fosters a competitive ecosystem, driving advancements in algorithms, hardware, and software. Moreover, the highly skilled workforce comprising AI experts, data scientists, and engineers, is contributing to the development of sophisticated models and applications thus representing another major growth-inducing factor.

Besides this, North America's strong emphasis on entrepreneurship and venture capital funding allows the growth of AI-driven startups that often pioneer groundbreaking applications, further propelling market expansion. Additionally, supportive government policies, such as tax incentives and funding for AI research, encourage innovation, and attract businesses and investments to the region. Furthermore, the well-established infrastructure, including robust cloud computing services and high-performance computing resources, facilitates the scalability and deployment of complex deep learning models across the region.

Competitive Landscape:

At present, key players in the market are adopting various strategies to strengthen their position and gain a competitive edge. Companies are investing heavily in research and development (R&D) to stay at the forefront of deep learning technology focusing on improving algorithms, developing novel architectures, and exploring new applications to offer cutting-edge solutions to their customers. Moreover, several companies are engaging in strategic acquisitions and partnerships to expand their offerings and capabilities. Key players are expanding their operations to new geographic regions to tap into emerging markets and reach a broader customer base, including establishing regional offices, forming partnerships with local companies, and adapting their offerings to suit regional needs. They are providing excellent customer support and training services for customer satisfaction and loyalty and investing in customer support teams and educational resources to ensure their clients can maximize the value of their solutions.

The report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

Amazon Web Services (AWS)

Google Inc.

IBM

Intel

Micron Technology

Microsoft Corporation

Nvidia

Qualcomm

Samsung Electronics

Sensory Inc.,

Pathmind, Inc.

Xilinx

Recent Developments:

In October 2020, NVIDIA AI and Microsoft Azure team collaborated to improve the AI-powered grammar checker in Microsoft word which can now tap into the NVIDIA triton inference server, ONNX Runtime, and Microsoft Azure machine learning (ML) to provide this smart experience.

In May 2022, Intel introduced its second-generation Habana AI deep learning processors in order to deliver high efficiency and high performance. Intel is executing its AI strategy to give customers numerous solution choices from the cloud to the edge, addressing the increasing number and complex nature of AI workloads.

In August 2022, Amazon web services introduced a new machine learning (ML) software through which medical records of patients can be analyzed for better treatment of patients and reduce expenses.

Key Questions Answered in This Report

• 1. What was the size of the global deep learning market in 2023?
• 2. What is the expected growth rate of the global deep learning market during 2024-2032?
• 3. What has been the impact of COVID-19 on the global deep learning market?
• 4. What are the key factors driving the global deep learning market?
• 5. What is the breakup of the global deep learning market based on the product type?
• 6. What is the breakup of the global deep learning market based on the application?
• 7. What is the breakup of the global deep learning market based on the end-use industry?
• 8. What are the key regions in the global deep learning market?
• 9. Who are the key players/companies in the global deep learning market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global Deep Learning Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Product Type

• 6.1 Software
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Services
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast
• 6.3 Hardware
  • 6.3.1 Market Trends
  • 6.3.2 Market Forecast

7 Market Breakup by Application

• 7.1 Image Recognition
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Signal Recognition
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Data Mining
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast
• 7.4 Others
  • 7.4.1 Market Trends
  • 7.4.2 Market Forecast

8 Market Breakup by End-Use Industry

• 8.1 Security
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Manufacturing
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Retail
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Automotive
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast
• 8.5 Healthcare
  • 8.5.1 Market Trends
  • 8.5.2 Market Forecast
• 8.6 Agriculture
  • 8.6.1 Market Trends
  • 8.6.2 Market Forecast
• 8.7 Others
  • 8.7.1 Market Trends
  • 8.7.2 Market Forecast

9 Market Breakup by Architecture

• 9.1 RNN
  • 9.1.1 Market Trends
  • 9.1.2 Market Forecast
• 9.2 CNN
  • 9.2.1 Market Trends
  • 9.2.2 Market Forecast
• 9.3 DBN
  • 9.3.1 Market Trends
  • 9.3.2 Market Forecast
• 9.4 DSN
  • 9.4.1 Market Trends
  • 9.4.2 Market Forecast
• 9.5 GRU
  • 9.5.1 Market Trends
  • 9.5.2 Market Forecast

10 Market Breakup by Region

• 10.1 North America
  • 10.1.1 United States
    • 10.1.1.1 Market Trends
    • 10.1.1.2 Market Forecast
  • 10.1.2 Canada
    • 10.1.2.1 Market Trends
    • 10.1.2.2 Market Forecast
• 10.2 Asia Pacific
  • 10.2.1 China
    • 10.2.1.1 Market Trends
    • 10.2.1.2 Market Forecast
  • 10.2.2 Japan
    • 10.2.2.1 Market Trends
    • 10.2.2.2 Market Forecast
  • 10.2.3 India
    • 10.2.3.1 Market Trends
    • 10.2.3.2 Market Forecast
  • 10.2.4 South Korea
    • 10.2.4.1 Market Trends
    • 10.2.4.2 Market Forecast
  • 10.2.5 Australia
    • 10.2.5.1 Market Trends
    • 10.2.5.2 Market Forecast
  • 10.2.6 Indonesia
    • 10.2.6.1 Market Trends
    • 10.2.6.2 Market Forecast
  • 10.2.7 Others
    • 10.2.7.1 Market Trends
    • 10.2.7.2 Market Forecast
• 10.3 Europe
  • 10.3.1 Germany
    • 10.3.1.1 Market Trends
    • 10.3.1.2 Market Forecast
  • 10.3.2 France
    • 10.3.2.1 Market Trends
    • 10.3.2.2 Market Forecast
  • 10.3.3 United Kingdom
    • 10.3.3.1 Market Trends
    • 10.3.3.2 Market Forecast
  • 10.3.4 Italy
    • 10.3.4.1 Market Trends
    • 10.3.4.2 Market Forecast
  • 10.3.5 Spain
    • 10.3.5.1 Market Trends
    • 10.3.5.2 Market Forecast
  • 10.3.6 Russia
    • 10.3.6.1 Market Trends
    • 10.3.6.2 Market Forecast
  • 10.3.7 Others
    • 10.3.7.1 Market Trends
    • 10.3.7.2 Market Forecast
• 10.4 Latin America
  • 10.4.1 Brazil
    • 10.4.1.1 Market Trends
    • 10.4.1.2 Market Forecast
  • 10.4.2 Mexico
    • 10.4.2.1 Market Trends
    • 10.4.2.2 Market Forecast
  • 10.4.3 Others
    • 10.4.3.1 Market Trends
    • 10.4.3.2 Market Forecast
• 10.5 Middle East and Africa
  • 10.5.1 Market Trends
  • 10.5.2 Market Breakup by Country
  • 10.5.3 Market Forecast

11 SWOT Analysis

• 11.1 Overview
• 11.2 Strengths
• 11.3 Weaknesses
• 11.4 Opportunities
• 11.5 Threats

12 Value Chain Analysis

13 Porters Five Forces Analysis

• 13.1 Overview
• 13.2 Bargaining Power of Buyers
• 13.3 Bargaining Power of Suppliers
• 13.4 Degree of Competition
• 13.5 Threat of New Entrants
• 13.6 Threat of Substitutes

14 Competitive Landscape

• 14.1 Market Structure
• 14.2 Key Players
• 14.3 Profiles of Key Players
  • 14.3.1 Amazon Web Services (AWS)
    • 14.3.1.1 Company Overview
    • 14.3.1.2 Product Portfolio
  • 14.3.2 Google Inc.
    • 14.3.2.1 Company Overview
    • 14.3.2.2 Product Portfolio
    • 14.3.2.3 SWOT Analysis
  • 14.3.3 IBM
    • 14.3.3.1 Company Overview
    • 14.3.3.2 Product Portfolio
  • 14.3.4 Intel
    • 14.3.4.1 Company Overview
    • 14.3.4.2 Product Portfolio
    • 14.3.4.3 Financials
    • 14.3.4.4 SWOT Analysis
  • 14.3.5 Micron Technology
    • 14.3.5.1 Company Overview
    • 14.3.5.2 Product Portfolio
    • 14.3.5.3 Financials
    • 14.3.5.4 SWOT Analysis
  • 14.3.6 Microsoft Corporation
    • 14.3.6.1 Company Overview
    • 14.3.6.2 Product Portfolio
    • 14.3.6.3 Financials
    • 14.3.6.4 SWOT Analysis
  • 14.3.7 Nvidia
    • 14.3.7.1 Company Overview
    • 14.3.7.2 Product Portfolio
    • 14.3.7.3 Financials
    • 14.3.7.4 SWOT Analysis
  • 14.3.8 Qualcomm
    • 14.3.8.1 Company Overview
    • 14.3.8.2 Product Portfolio
    • 14.3.8.3 Financials
    • 14.3.8.4 SWOT Analysis
  • 14.3.9 Samsung Electronics
    • 14.3.9.1 Company Overview
    • 14.3.9.2 Product Portfolio
  • 14.3.10 Sensory Inc.
    • 14.3.10.1 Company Overview
    • 14.3.10.2 Product Portfolio
  • 14.3.11 Pathmind Inc.
    • 14.3.11.1 Company Overview
    • 14.3.11.2 Product Portfolio
  • 14.3.12 Xilinx
    • 14.3.12.1 Company Overview
    • 14.3.12.2 Product Portfolio
    • 14.3.12.3 Financials
    • 14.3.12.4 SWOT Analysis