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Deep Learning Processor Market - Forecasts from 2020 to 2025

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    Report

  • 130 Pages
  • March 2020
  • Region: Global
  • Knowledge Sourcing Intelligence LLP
  • ID: 5009308
Global Deep Learning Chip market is projected to grow at a CAGR of 42.49% during the forecast period, reaching a total market size of US$69.986 billion in 2025 from US$8.363 billion in 2019

Deep learning is a subset of machine learning and machine learning is a subset of artificial intelligence. The market for deep learning processor 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 processor. The increasing data generated nowadays from all technical sources is growing the requirement for faster and advanced deep learning processors for faster analysis. Shifting trend towards quantum computing provides a great opportunity for the expansion of the deep learning processor market during the next five years. 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 in the near future. Other factors that offer growth potential for the deep learning processor market in the near future include rising investments in AI startups and R&D in smart robotics.

However, the lack of a skilled workforce is acting as a restraining factor for the market growth of the deep learning processor market. To manage deep learning software and its applications there is a need for a worker who has the ability to process or carry out complex algorithms for AI development. Moreover, the management of AI and automated systems is difficult at times. For getting the maximum output from deep learning, it requires exceptional software engineering skills and notable experience to deal with distributed and concurrent programming and debugging with communications protocols.

The Global Deep Learning Processor market is segmented by chip type, by technology, by the end-user industry, and by geography. By chip type, the market is segmented into GPU, ASIC, CPU, and FPGA. On the basis of technology, the market is segmented by System-On-Chip (SIC), System-In-Package (SIP), Multi-Chip Module, and others. The market is further segmented by the end-user industry as consumer electronics, communication and technology, retail, healthcare, automotive, and others.

Chip type GPU has a significant share in the market

GPU (graphics processing units) accounts for a significant market share by chip type. It is increasingly being used for gaming and video viewing purposes. But now with advancing technology, GPU is majorly used for high-resolution images and artificial intelligence (AI). The demand is also increasing due to the use of low power technology. 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 to grow at a substantial CAGR during the forecast period. The quantum computing is highly used these days by big multinational and information technology companies owing to its ability to solve complex algorithms in the fastest time possible, therefore positively impacting the market growth of deep learning chip. FPGAs chip market is growing as it makes configure 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 requirement of the industry the companies are using ASICs chips for better performance and efficiency.

By technology, System-On-Chip has a significant share in the market

The growing market for smartphones and tablets is increasing the demand for System-On-Chip processor in the market. 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 perfectly suitable for smartphones. Smartphones and tablets are enabled with System-on-chip for providing 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 one of the major segments

A deep learning processor is majorly 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 processor. Companies are using machine learning chips in smartphones for improving its features and maximizing capabilities like a faster processor and improved multi-tasking ability. Artificial intelligence applications are coming embedded within the smartphones and tablets for providing better user interface and customer experience making the demand for deep learning processor grow. New devices are coming with advanced technologies for industries like healthcare and communication & technology which are highly using deep learning processors for faster work and higher efficiency. The retail industry will witness a decent CAGR between 2019 and 2025 owing to the booming e-commerce industry. The rising application of deep learning processor in this industry is to improve customer experience and by using artificial intelligence and augmented reality which, in turn, is fueling the market growth of deep learning processor.

North America is the major regional market

Regionally, the global deep learning processor market is classified into North America, South America, Europe, Middle East and Africa, and Asia Pacific. North America accounted for the major market share in 2019 and will continue its dominance throughout the forecast period. 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. is also bolstering the market growth in this region. APAC and European regional market for deep learning processor will witness a significant market growth rate during the next five years.

Market Players and Competitive Intelligence

The competitive intelligence section deals with major players in the market, their market shares, growth strategies, products, financials, and recent investments among others. Key industry participants profiled as part of this section are NVIDIA Corporation, Microsoft, Arm Limited, Samsung, Alphabet, Qualcomm, Graphcore, Advanced Micro Devices, Adapteva, eSilicon Corporation, and Intel Corporation among others.

Segmentation

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

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
  • Others

Table of Contents

1. Introduction
1.1. Market Definition
1.2. Market Segmentation
2. Research Methodology
2.1. Research Data
2.2. Assumptions
3. Executive Summary
3.1. Research Highlights
4. Market Dynamics
4.1. Market Drivers
4.2. Market Restraints
4.3. Porters 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
5. Global Deep Learning Processor Market Analysis, By Chip Type
5.1. Introduction
5.2. GPU
5.3. ASIC
5.4. CPU
5.5. FPGA
6. Global Deep Learning Processor Market Analysis, By Technology
6.1. Introduction
6.2. System-On-Chip (SIC)
6.3. System-IN-Package (SIP)
6.4. Multi-Chip Module
6.5. Others
7. Global Deep Learning Processor Market Analysis, By End-User industry
7.1. Introduction
7.2. Consumer Electronics
7.3. Communication & Technology
7.4. Retail
7.5. Healthcare
7.6. Automotive
7.7. Others
8. Global Deep Learning Processor Market Analysis, By Geography
8.1. Introduction
8.2. North America
8.2.1. North America Deep Learning Processor Market, By Chip Type
8.2.2. North America Deep Learning Processor Market, By Technology
8.2.3. North America Deep Learning Processor Market, By End-User Industry
8.2.4. By Country
8.2.4.1. USA
8.2.4.2. Canada
8.2.4.3. Mexico
8.3. South America
8.3.1. South America Deep Learning Processor Market, By Chip Type
8.3.2. South America Deep Learning Processor Market, By Technology
8.3.3. South America Deep Learning Processor Market, By End-User Industry
8.3.4. By Country
8.3.4.1. Brazil
8.3.4.2. Argentina
8.3.4.3. Others
8.4. Europe
8.4.1. Europe Machine Deep Processor Market, By Chip Type
8.4.2. Europe Machine Deep Processor Market, By Technology
8.4.3. Europe Machine Deep Processor Market, By End-User Industry
8.4.4. By Country
8.4.4.1. Germany
8.4.4.2. France
8.4.4.3. United Kingdom
8.4.4.4. Spain
8.4.4.5. Others
8.5. Middle East and Africa
8.5.1. Middle East and Africa Deep Learning Processor Market, By Chip Type
8.5.2. Middle East and Africa Deep Learning Processor Market, By Technology
8.5.3. Middle East and Africa Deep Learning Processor Market, By End-User Industry
8.5.4. By Country
8.5.4.1. Saudi Arabia
8.5.4.2. Israel
8.5.4.3. UAE
8.5.4.4. Others
8.6. Asia Pacific
8.6.1. Asia Pacific Deep Learning Processor Market, By Chip Type
8.6.2. Asia Pacific Deep Learning Processor Market, By Technology
8.6.3. Asia Pacific Deep Learning Processor Market, By End-User Industry
8.6.4. By Country
8.6.4.1. China
8.6.4.2. Japan
8.6.4.3. South Korea
8.6.4.4. India
8.6.4.5. Others
9. Competitive Environment and Analysis
9.1. Major Players and Strategy Analysis
9.2. Emerging Players and Market Lucrativeness
9.3. Mergers, Acquisitions, Agreements, and Collaborations
9.4. Vendor Competitiveness Matrix
10. Company Profiles
10.1. ARM Limited
10.2. NVIDIA Corporation
10.3. Microsoft
10.4. Samsung
10.5. Alphabet
10.6. Qualcomm
10.7. Graphcore
10.8. Advanced Micro Devices
10.9. Adapteva
10.10. eSilicon Corporation
10.11. Intel Corporation

Companies Mentioned

  • ARM Limited
  • NVIDIA Corporation
  • Microsoft
  • Samsung
  • Alphabet
  • Qualcomm
  • Graphcore
  • Advanced Micro Devices
  • Adapteva
  • eSilicon Corporation
  • Intel Corporation

Methodology

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