Deep Learning Chip Market Global Industry Scenario, Size, Share, Trends, Analysis and Forecast, 2019 - 2027

Global Deep Learning Chipset Market

Deep learning is a sub-set of machine learning, which is a sub-set of artificial intelligence (AI) that is achieved to perform tasks related to AI. Deep learning works as a brain, which has been penetrating in several industries around the world. This technology is achieved with software, such as computer vision, voice recognition, speech synthesis, machine translation, game playing, drug discovery, and robotics. Deep learning chips are specialized Silicon chips, which incorporate AI and machine learning technology. The global deep learning chipset market is expected to witness significant growth during the forecast period.

North America leads the global deep learning chip market, and it is anticipated to be the highest revenue contributor throughout the forecast period. Deep learning chip development is backed by large-scale investment from technological giants to develop patterns from a huge amount of generated data. The rise of quantum computing and the implementation of deep learning chips in robotics is driving the growth of the deep learning chip market in the North American countries. 

Emergence of quantum computing and enhanced implementation of deep learning chips in robotics drive the growth of the global deep learning chip market considerably. In addition, emergence of autonomous robotics-robots that develop and control themselves autonomously-is anticipated to provide potential growth opportunities for the market. However, dearth of skilled workforce is one of the major restraints of the market. Most of the tasks, such as testing, bug fixing, cloud implementation, and others, are taken over by deep learning chips; however, delivery of such tasks lacks essential skillsets.

The major companies profiled in the report include AMD (Advanced Micro Devices), Google, Inc., Intel Corporation, NVIDIA, Baidu, Bitmain Technologies, Qualcomm, Amazon, Xilinx, and Samsung.

The global deep learning chipset market is segmented based on chip type, industry vertical, technology, and geography. By chip type, the market is categorized into graphics processing unit (GPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), central processing unit (CPU), and others. System-on-chip, system-in-package, multi-chip module, and others are the technologies taken into consideration. The industry verticals taken into account in the study include media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others.

Geographically, the deep learning chip industry is sub-segmented into North America, Europe, Asia-Pacific, and RoW.

The significant impacting factors in the global deep learning chip industry include increase in demand for smart homes and smart cities, rise in investments in AI startups, emergence of quantum computing, growth in the number of AI applications, dearth of skilled workforce, increase in adoption of deep learning chips in the developing regions, and development of smarter robots. Each of these factors is anticipated to have a definite impact on the deep learning chipset market during the forecast period.

AI provides impetus to initiate smart city programs in developing countries, such as India. Tools and technologies that are artificially intelligent possess a massive potential to transform interconnected digital homes and smart cities. Furthermore, creation of a chip that embeds an inbuilt AI network has emerged as an opportunity for the deep learning chip market.

Multiple countries, especially the U.S., witness a considerable growth in tech start-ups every year, which are backed by various venture capitalists and venture capitals, thus increasing the market scope. Various key players have been innovating to build a dedicated platform; for instance, Mythic’s platform has an advantage of processing digital/analog calculations in memory, which results in enhanced performance, accuracy, and power life. Furthermore, surge in need to integrate video surveillance and AI and rise in government spending for cyber security solutions that are integrated with real-time analytics and AI are anticipated to boost the growth of the deep learning chipset market.

Quantum computers take seconds to complete a calculation that would otherwise take thousands of years; for instance, Google has a quantum computer that is 100 million times faster than today’s computing systems. Quantum computers are an innovative transformation of artificial intelligence, big data, and machine learning. Thus, emergence of quantum computing has fueled the growth of the deep learning chipset market. Furthermore, processor performance has improved around five times since Intel's introduction of the Pentium processors. Firstly, because of the smaller size of transistors, which has collapsed from 800 nm to 16 nm. This enables to create processors with billions of transistors, which operate in the gigahertz range and increase the computational power drastically. Secondly, owing to the improved graphic processing units (GPUs) over the traditional central processing units (CPUs). Hence, a single processor performs complex calculations in seconds, which during the 90s would have required multiple lifetimes. With the help of the internet's size and scale, deep learning handles large data sets at a very low cost. These factors have been helping to drive the growth of the global deep learning chip industry.

AI consists of complex algorithms for its development. In addition, management of AI and automated systems is difficult at times. This requires exceptional software engineering skills and a notable experience to deal with distributed and concurrent programming or debugging with communication protocols. However, many regions, particularly the emerging economies, lack people with such skills. Hence, dearth of a skilled workforce is a prominent restraining force of the deep learning chip industry.

Recent developments in the emerging economies, such as China and India, across various industry verticals, which include media & advertising, finance, retail, healthcare, automotive & transportation, and others, have created a major growth potential for AI. The time and cost benefits provided by AI are the major growth factors leading to its increased adoption in the developing regions. All these factors together fuel the growth of the market.

Many players have been building superior robot brains, which would enable machines to operate autonomously by deployment; for instance, Rethink Robotics’ Baxter is a research robot, which is trained accordingly. Similarly, human-like robots are invented by Hanson Robotics, which carry a peculiar conversation and recall personal history. Furthermore, development of smarter virtual assistants is opportunistic for the overall market. A notable illustration is Jarvis Corp, which is a start-up in the conceptual phases. It has been building a virtual assistant that answers questions by accessing the internet and acting as an internet server and acts as a control for connected devices.

Deep Learning Chip Market Key Segments:

By Chip Type

• GPU
• ASIC
• FPGA
• CPU
• Others

By Technology

• System-on-chip (SoC)
• System-in-package (SIP)
• Multi-chip module
• Others

By Industry Vertical

• Media & advertising
• BFSI
• IT & telecom
• Retail
• Healthcare
• Automotive & transportation
• Others

By Region

• North America
• U.S.
• Canada
• Mexico
• Europe
• UK
• Germany
• France
• Russia
• Rest of Europe
• Asia-Pacific
• China
• Japan
• India
• Australia
• Rest of Asia-Pacific
• LAMEA
• Latin America
• Middle East
• Africa

Key Market Players Profiled

• AMD (Advanced Micro Devices)
• Google, Inc.
• Intel Corporation
• NVIDIA
• Baidu
• Bitmain Technologies
• Qualcomm
• Amazon
• Xilinx
• Samsung