Machine Learning Processor Market - Forecasts from 2024 to 2029

Machine Learning Processor Market - Forecasts from 2024 to 2029

The machine learning processor market is evaluated at US$3.843 billion for the year 2022 growing at a CAGR of 19.94% reaching the market size of US$13.917 billion by the year 2029.

The global machine learning processor market is rising due to the growing popularity of artificial intelligence and the trend toward big data. Increasing IoT devices is further driving the demand for machine learning processors, thereby driving market growth. The increasing number of AI applications, improved computer power, and falling hardware costs are driving machine learning processor sales. The high adoption of artificial intelligence by various industries for automation purposes is driving the market for machine learning processors. The increasing amount of data generated nowadays from all technical sources is growing the requirement for faster and more advanced machine learning processors for faster analysis. Companies are heavily investing in research and development to introduce new and updated products to occupy a larger market share. The machine learning processor is improving consumer services and reducing operational costs, which are significantly driving the market growth.

However, the lack of a skilled workforce and the absence of standards and protocols are restraining the market growth of machine learning processors. AI is a complex system, and developing, managing, and implementing, it requires employees with certain skill sets.

MARKET DRIVERS:

Increasing adoption of machine learning (ML) and artificial intelligence (AI) technologies.

The machine learning processors market is greatly impacted by the growing use of artificial intelligence (AI) and machine learning (ML) technologies. The need for specialized processors that can effectively handle the computational complexities of workloads like image recognition, natural language processing, and predictive analytics is growing as companies in a variety of industries incorporate ML and AI into their operations. Machine learning processors, as opposed to conventional processors, are designed expressly for the matrix computations and parallel processing used in machine learning algorithms. This allows for quicker and more effective model inference and training.
Rising complexity of AI Models.

The machine learning processors market is significantly impacted by the growing complexity of machine learning (ML) models, which calls for improvements in hardware architecture and capabilities. To effectively perform complicated mathematical operations during both training and inference, there is an increasing demand for processors that can supply increased computing capacity as machine learning (ML) models, especially those using deep learning, get more detailed and advanced. Owing to this need, specialized hardware designs have been created, including GPUs, TPUs, and other accelerators. These architectures are made expressly to handle the complicated matrix operations and parallel processing that come with large-scale machine-learning models. The market's reaction, which includes processors with many cores and parallel processing units, demonstrates a greater emphasis on optimization for parallel computing.
GPU is anticipated to have a significant share of the market

GPU (graphics processing units) are increasingly being used for gaming and video viewing purposes. Advancing and new technology like AR (Augmented Reality) are driving the demand for GPU processors in the market. The CPU is expected to witness a decent CAGR during the forecast period due to the increasing use of Quantum computing. Quantum computing takes only a few seconds to complete a calculation that otherwise may take thousands of years. The FPGA is driving the machine learning processor market as new and advanced technology is coming every year and people are continuously updating according to the current trend, and the FPGA processor makes it faster to configure. ASIC processors are increasingly being used by different industries for carrying out specific tasks according to the requirements of the industry, thereby positively impacting market growth.

By technology, System-On-Chip is anticipated to be one of the major segments.s

System-on-chip has a noteworthy share in the global machine learning processor market on account of the growing market for smartphones. System-On-Chip includes a central processing unit, memory, input/output ports, and secondary storage, all on a single substrate or microprocessor, the size of a coin, which is perfectly suitable for smartphones. System-on-chip is usually used in smartphones for better performance and faster processing of multi-task activities. System-in-package is increasingly boosting the market for machine learning processors due to its usage in 3D development. The heavy inflow of investments by market players into this technology is expanding its scope of application from smartphones and media players to many more applications across a wider range of industries. Since this technology supports a wider range of integration techniques than many other technologies, end-users seeking more flexibility in solutions are showing a continuously increasing adoption of this technology, thus fueling the market growth.

Consumer Electronics is predicted to be one of the major industries for machine learning processor market players.

The consumer electronics segment is predicted to account for a significant market share during the forecast period. The increasing advancement in technology is building the market for better devices with improved applications. The future of technology is dependent on the increasing use of artificial intelligence and big data. Companies are using a machine learning processor in smartphones to improve their features and maximize capabilities, like a faster processor and improved multi-tasking ability. Smartphones and tablets are embedded with artificial intelligence to enhance customer experience and a better user interface. Hence, the growing demand for advanced consumer electronics is spurring the demand for machine learning processors. Increased usage of advanced technologies in healthcare and communication & technology is giving rise to the use of machine learning processors as new devices are highly embedded with machine learning processors for better performance. The retail sector is expected to experience significant market growth during the forecast period owing to the booming global e-commerce industry.

By geography, North America is anticipated to be the largest market.

Regionally, the global machine learning processor market is classified into North America, South America, Europe, the Middle East and Africa, and the Asia Pacific. North America is expected to have a notable market share in the global machine learning processor market owing to the early adoption of advanced technologies and the presence of major market players in the region. Global software and hardware companies present in this region are increasingly using artificial intelligence, big data, and augmented reality to improve technology and provide better services to customers. High investments in artificial intelligence will further bolster the market growth of machine learning processors across this region throughout the forecast period.

Key Developments:

In October 2023, a strategic alliance was established between Renesas Electronics Corporation, a leading producer of innovative semiconductor technologies, and EdgeCortix, a top supplier of edge Artificial Intelligence (AI) processing systems that are energy-efficient. Renesas has contributed to EdgeCortix's most recent fundraising round in tandem with the strategic partnership. Through this collaboration and investment, EdgeCortix will provide Renesas exclusive access to its cutting-edge technology.

Segmentation:

By Processor 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