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The machine learning chips market size is estimated to increase by USD 36.44 billion and grow at a CAGR of 36.5% between 2023 and 2028. Market growth hinges on various factors, notably the proliferation of online data centers housing numerous servers powered by central processing units (CPUs), alongside the utilization of artificial intelligence (AI) technology for enhancing energy efficiency, infrastructure management, server optimization, security, and diverse applications. However, challenges persist, such as the global semiconductor chip shortage, the cyclicality inherent in the semiconductor industry, and the complexities associated with comprehending data.
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The market share growth by the BFSI segment will be significant during the forecast period. They have revolutionized the BFSI industry. The entire BFSI industry is driven by the customer data that financial companies have access to. AI is used by a number of marketing technologies, including Data Management Platforms (DMPs) and Customer Data Platforms (CDPs), to improve and personalize user engagements across a variety of digital channels. Cookies are used to track a user's online activity so that more relevant messages can be sent to them. Marketing has become more targeted and pertinent thanks to AI. As a result, businesses are able to increase their online revenue and engage customers across multiple touchpoints.
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The BFSI segment was valued at USD 1.53 billion in 2018. The marketing landscape has been transformed completely by the emergence and use of machine learning and AI. The gap between the marketer and the customer is growing smaller steadily due to this. Marketers in the BFSI industry can improve their current and upcoming marketing campaigns by understanding their customers' past behavior better. In the insurance industry, the usage of AI can help in reducing operating costs and, at the same time, can increase customer satisfaction during the renewal of policies, claims processing, and other services. These factors are anticipated to augment the demand from the BFSI industry, thus, propelling the growth of the market during the forecast period.
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APAC is estimated to contribute 40% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The growth of the market in North America is driven by increasing investments in autonomous vehicles. These vehicles are integrated with advanced systems, such as advanced driver assistance systems (ADAS), heads-up display (HUD), light detection and ranging (LiDAR), and radio detection and ranging (RADAR). Electronic components such as sensors, microcontrollers, microprocessors, and other radio frequency (RF) components generate and process a large amount of data in real time.
Several automotive OEMs are working toward commercializing autonomous vehicles, which is providing ample opportunities for machine learning chip manufacturers to tap into the untapped potential of the market. The integration of advanced human-machine interface (HMI) technologies, along with developments in wired and wireless communication technologies for automotive applications, is expected to have a positive impact on the growth of the market in North America during the forecast period.
The Machine Learning Chip Market is witnessing significant growth due to the increasing demand for advanced computing solutions in various industries. Machine learning algorithms require high computational power for algorithmic calculations and neural network architectures, leading to the popularity of specialized chips such as GPU (Graphics Processing Units), ASIC (Application-Specific Integrated Circuits), and FPGA (Field-Programmable Gate Arrays). GPUs are widely used for AI tasks due to their ability to handle parallel processing, making them ideal for media and advertising, IT and telecom, and quantum computing applications. ASICs and FPGAs, on the other hand, offer customized solutions for specific AI tasks and are commonly used in smart cities, smart homes, and autonomous vehicles. Systems on Chip (SoC), System in Package (SiP), and Multi Chip Module (MCM) are also gaining popularity due to their integration of multiple functions into a single chip or package.
Also, the market is witnessing rapid evolution fueled by advancements in deep learning algorithms and the proliferation of cloud computing. Key industries like the media and advertising industry are leveraging structured data and integrated circuits to enhance audience targeting and engagement. Meanwhile, general-purpose processors and specialized multi-chip modules support diverse applications from X-rays and CT scans in healthcare to language translation and algorithmic trading in financial services. The demand for machine learning models drives innovation across sectors, supported by a skilled AI workforce and research institutions pushing the boundaries of natural language processing and computer vision. As network security and industry verticals adopt ML chips, emerging technologies like quantum computers and autonomous robotics are poised to shape the future of high-tech products and smart gadgets.
The global market is experiencing robust growth driven by the adoption of machine learning chips in autonomous vehicles. Automotive companies recognize the pivotal role of these chips in achieving high levels of vehicle automation, fueling increased demand in this sector. With sensors, cameras, radar, LIDAR, and ultrasonic instruments generating vast amounts of data, these processors analyze the information to make split-second decisions on the road. Machine learning chips enable advanced features like ADAS, intuitive user interfaces, and automotive cloud services, offering advantages such as object recognition, reduced power consumption, and improved perception. This trend presents lucrative opportunities for vendors to expand their market presence and revenue streams.
Increasing investments in AI start-ups is the primary trend shaping the global market. AI technology is still in the development phase, and hence, its implementation is growing rapidly across many industries. Several companies, such as start-ups, are entering the market to capitalize on the growing demand for AI technology. Due to the huge growth potential of the global market, several start-ups have been receiving significant investments from venture capitalists and major chip manufacturers for the development of AI platforms and chipsets.
For instance, in May 2021, Shanghai-based start-up Innostar Semiconductor raised USD 100 million funding in a pre-series A round funding, which was led by Shanghai Lianhe Investment and joined by New Alliance as well as new investors Atlas Capital and KQ Capital. The company will use the funding for the development of storage and resistive RAM chips. In May 2020, Tessolve received funding of USD 40.0 million as a private equity investment from Novo Tellus Capital Partners. The company will use this funding to expand its chip and ASIC design business and embedded service offerings. These factors will drive the growth of the market in focus during the forecast period.
The cyclical nature of the semiconductor industry is a major challenge impeding the growth of the global market. The fluctuations in demand for electronic products, such as consumer electronic devices and mobile devices, make forecasting in the global market extremely difficult. These fluctuations can also lead to the oversupply or undersupply of semiconductor ICs. In the case of oversupply, machine learning chip manufacturers can fulfill the demand for semiconductor ICs without expanding their manufacturing capacity, which reduces their capital spending. In the case of undersupply, the demand will be hard to fulfill due to the lengthy manufacturing process.
Thus, vendors might manufacture based on forecasts of customer demand, which may fluctuate significantly. Such fluctuations may lead to high inventory and manufacturing costs before actual sales take place. Hence, vendors would require high amounts of working capital to meet these costs. Also, inaccurate forecasts or cancellations/delays in orders for machine learning and other types of chips can adversely affect the operations of vendors, which leads to a huge loss for vendors in the market. Such factors may hinder market growth during the forecast period.
The market report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their growth strategies.
Global Market Customer Landscape
Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
Advanced Micro Devices Inc.- The company offers machine learning chips such as AMD Instinct. This segment focuses on offering CPUs, APUs, and chipsets for desktop and notebook personal computers.
The research report also includes detailed analyses of the competitive landscape of the market and information about 15 market players, including:
Qualitative and quantitative analysis of vendors has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize vendors as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize vendors as dominant, leading, strong, tentative, and weak.
The market research report provides comprehensive data (region wise segment analysis), with forecasts and estimates in "USD Billion" for the period 2024 to 2028, as well as historical data from 2018 to 2022 for the following segments.
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The market is witnessing significant growth due to the increasing adoption of Artificial Intelligence (AI) technology in various industries. Machine Learning Chips are integral components for processing AI tasks, including GPU (Graphics Processing Units), ASIC (Application-Specific Integrated Circuits), FPG (Field-Programmable Gate Arrays), and System on Chip (SoC). GPU, with its parallel processing capabilities, is widely used in the gaming industry and IT & Telecom sectors for handling complex AI algorithms. ASICs, custom-designed for specific AI tasks, offer high performance and energy efficiency. FPGs provide flexibility in designing and implementing AI algorithms. The market is segmented into Media and Advertising, IT and Telecom, Healthcare, Automotive and Transportation, Storage, Computing, Networking, and others. AI applications in these industries include Big Data Analytics, Fraud Detection Systems, Cybersecurity, and Database Management. The growing demand for AI in Digitalization, Robotics, and Smart Cities & Homes is driving the Machine Learning Chips Market.
However, cyber attacks pose a significant threat to the security of AI systems, necessitating robust cybersecurity measures. The Information Technology industry and Telecommunication industry are major consumers of Machine Learning Chips due to their extensive use of AI for data processing and network optimization. The Gaming industry also contributes significantly to the market growth due to the increasing popularity of AI in gaming applications. The market is experiencing robust growth, driven by advancements in Artificial Intelligence technology and the demand for specialized hardware like CPU and multi-chip modules. These chips are designed to accelerate complex computations needed for AI tasks, making them crucial for applications in various sectors including media and advertising, where targeted algorithms enhance audience engagement. Moreover, integrated circuits and general-purpose processors are evolving to meet the computational demands of ML algorithms, facilitating faster data processing. In the healthcare sector, ML chips analyze medical images such as X-rays, improving diagnostic accuracy and treatment planning. As AI continues to permeate industries, the market for machine learning chips is poised for further expansion, driving innovation and efficiency across diverse applications.
Market Scope |
|
Report Coverage |
Details |
Page number |
190 |
Base year |
2023 |
Historic period |
2018 - 2022 |
Forecast period |
2024-2028 |
Growth momentum & CAGR |
Accelerate at a CAGR of 36.5% |
Market growth 2024-2028 |
USD 36.45 billion |
Market structure |
Fragmented |
YoY growth 2023-2024(%) |
26.88 |
Regional analysis |
North America, Europe, APAC, South America, and Middle East and Africa |
Performing market contribution |
APAC at 40% |
Key countries |
US, China, UK, Germany, and Taiwan |
Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
Key companies profiled |
Advanced Micro Devices Inc., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc. |
Market dynamics |
Parent market analysis, Market Forecasting, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, Market growth and Forecasting, COVID 19 impact and recovery analysis and future consumer dynamics, Market condition analysis for the market forecast period |
Customization purview |
If our market report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
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1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by End-user
7 Market Segmentation by Technology
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Opportunity/Restraints
11 Competitive Landscape
12 Competitive Analysis
13 Appendix
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