According to a survey by market research agency MR, the global sales of deep learning chipsets in 2022 will be $4.7 billion. By the end of the forecast period, the market valuation is expected to reach USD 72.8 billion, growing at a CAGR of 27.9% from 2023 to 2033. The deep learning chipset market in system-on-chip (SOC) production is expected to grow at a compound annual rate (CAGR) of more than 27.5% between 2023 and 2033, with broad market development space and possibly significant revenue.
A deep learning chipset is a chip product designed to process deep learning algorithms, which accelerates the execution of deep learning tasks and improves computing efficiency and accuracy by optimizing the hardware architecture.
Peculiarity:
Highly flexible architecture: Deep learning chipsets have a flexible architecture that is capable of handling complex data patterns and features, learning from raw data and continuously optimizing.
Powerful computing power: According to the characteristics of deep learning algorithms, the deep learning chipset is optimized in computing power and can handle large-scale datasets and complex models.
Low power consumption: Deep learning chipsets typically have lower power consumption when performing deep learning tasks than traditional CPUs and GPUs, making them beneficial for applications in mobile devices and other energy-constrained environments.
Figure: The deep learning chipset market is expanding
Fields of application
Deep learning chipsets have a wide range of applications in many fields, including:
Consumer market: For example, smartphones, smart home devices, etc., deep learning chipsets can provide more intelligent interactive experiences and more accurate services.
Aerospace, Military & Defense: Plays an important role in image processing, target recognition, autonomous navigation, and more.
Automotive: Autonomous vehicles need to process large amounts of sensor data in real time, and deep learning chipsets can provide powerful computing power.
Industry: play an important role in intelligent manufacturing, quality control, equipment maintenance, etc., to improve production efficiency and product quality.
The deep learning chipset market can be segmented based on different types and applications. Among them, graphics processing units (GPUs), central processing units (CPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs) are the mainstream product types in the market. In terms of applications, consumer electronics, automotive, industrial, and healthcare are the main application areas for deep learning chipsets.
The regional analysis shows that the North America region dominates the market due to its strong research environment and concentration of large technology companies. The Asia-Pacific region, especially China and Korea, is experiencing rapid market growth due to its strategic focus and investments in artificial intelligence. Some key players in the global deep learning chipset market include IBM, Graphcore, CEVA, AMD, NVIDIA, and Intel, among others. These companies strengthen their market position through strategic developments such as mergers and acquisitions, partnerships, and new product development.
The latest trends in the deep learning chipset market include increasing the number of processing cores and adopting mixed-precision computing techniques to improve performance and energy efficiency. In the future, with the continuous advancement of technology and the further expansion of application fields, the deep learning chipset market is expected to continue to maintain a strong growth momentum.
