Deep Learning Processor Market Size, Share, Opportunities, And Trends By Chip Type (GPU, ASIC, CPU, FPGA), By Technology (System-On-Chip (SIC), System-IN-Package (SIP), Multi-Chip Module, Others ), By End-User Industry (Consumer Electronics, Communication & Technology, Retail, Healthcare, Automotive, Others), And By Geography - Forecasts From 2024 To 2029

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.

Deep Learning Processor Market Scope:

 

Report Metric	Details
Market Size Value in 2022	US$3.084 billion
Market Size Value in 2029	US$12.291 billion
Growth Rate	CAGR of 21.83% from 2022 to 2029
Base Year	2022
Forecast Period	2024 – 2029
Forecast Unit (Value)	USD Billion
Segments Covered	Chip Type Technology Industry Vertical Geography
Companies Covered	ARM Limited NVIDIA Corporation Microsoft Samsung Qualcomm And more
Regions Covered	North America, South America, Europe, Middle East and Africa, Asia Pacific
Customization Scope	Free report customization with purchase

 

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

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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

ARM Limited

NVIDIA Corporation

Microsoft

Samsung

Qualcomm

Graphcore

Advanced Micro Devices

Adapteva

Intel Corporation