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The deep learning chips market size is estimated to increase by USD 16.52 billion and grow at a CAGR of 34.58% between 2022 and 2027. Several factors contribute to the market's growth, such as the increasing utilization of deep learning chips in autonomous vehicles, the expanding array of AI applications, and the uptick in deep learning chip adoption within data centers. However, challenges exist, including a shortage of technically proficient personnel for deep learning chip development, ethical concerns, and the significant manufacturing expenses associated with deep learning chips.
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This market report extensively covers market segmentation by technology (system-on-chip, system-in-package, multi-chip module, and others), end-user (BFSI, IT and telecom, media and advertising, and others), and geography (North America, Europe, APAC, South America, and Middle East and Africa). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2017 to 2021.
The market share growth by the system-on-chip segment will be significant during the forecast period. SoC is becoming increasingly popular for its versatility, power, and efficiency in performing complex computational tasks. SoC is a highly integrated microchip that combines all the necessary components of a computer or other electronic system onto a single piece of silicon to perform essential functions, such as processing and communications.
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The system-on-chip segment was valued at USD 831.25 million in 2017. The SoC is ideal for deep learning applications since it integrates CPUs, GPUs, and the necessary memory on a single chip. This integration provides a higher level of performance and energy efficiency, making it an attractive option for device manufacturers to power their products. The SoC is proving to be an essential technology for the deployment of deep learning technology across multiple markets, such as autonomous vehicles, healthcare, retail, and manufacturing. These industries require complex applications that can handle massive amounts of data and execute complex algorithms. The SoC is capable of this, given its powerful processing capabilities and efficiency. Such factors will increase the market growth during the forecast period.
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North America is estimated to contribute 35% 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 deep learning chip market in North America is witnessing rapid growth due to the emergence of new technologies in smart devices and the increasing demand for these applications in various industries, such as healthcare, retail, and automotive. One of the major factors driving the growth of the deep learning chip market in North America is the increasing use of deep learning algorithms for improving the accuracy of image, speech, and signal recognition. Such factors will increase the market growth in this region during the forecast period.
The market is experiencing robust growth, driven by several key factors that are shaping the landscape of AI hardware development and deployment. One of the primary market drivers is the escalating demand for advanced AI hardware solutions across various industries. With the proliferation of artificial intelligence applications in sectors such as healthcare, finance, automotive, and retail, there is a growing need for specialized hardware accelerators tailored to support deep learning algorithms and neural network processing. This demand is fueling innovation and investment in AI chip technologies, driving the development of neural network processors, machine learning chips, and deep learning accelerators.
The market features a diverse array of chips tailored for different needs. GPU chips offer high parallel processing power, while CPU chips provide general-purpose computing. ASIC chips and FPGA chips excel in specific tasks and reconfigurability, respectively. High-performance computing chips cater to demanding applications, and embedded AI chips are ideal for edge devices. AI inference chips focus on executing trained models efficiently, whereas AI training chips are designed for model training. On-device AI chips bring AI capabilities to mobile and IoT devices. Neural processing units and AI co-processors enhance AI performance. These chips are pivotal in advancing AI chip integration, AI chip optimization, AI chip performance, AI chip scalability, AI chip efficiency, AI chip reliability, and AI chip security, fostering AI chip applications and use cases across industries. Continued AI chip innovations in the field are driving the market forward.
The rise in the adoption in autonomous vehicles is the key factor driving the growth of the global market. The growth of the market is driven by rapid development and investment in the implementation in self-driving cars. Many automotive companies recognize the importance of using the chips to achieve the highest level of automation in their vehicles, increasing the demand in this area. Achieving a high degree of automation (Level 4/5 automation), also known as full automation, requires the high computing power of the onboard processor.
Moreover, autonomous vehicles are integrated with advanced features such as ADASs, heads-up displays (HUDs), multimodal and intuitive user interfaces, and new-generation automotive cloud services. In ADASs, the deep learning concept has more advantages over traditional algorithms. For instance, deep learning helps in recognizing and detecting multiple objects, reduces power consumption, enables prediction and the recognition of objects, improves perception, and supports object classification. Thus, such factors are expected to increase the demand, which will drive the growth of the market during the forecast period.
Recent development is the primary trend shaping the global market growth. They have evolved significantly recently, becoming more powerful and efficient. Additionally, developers are actively working on creating new architectures and optimizing algorithms to improve the performance of tasks such as image and speech recognition, natural language processing, and autonomous driving. These advances are making AI more accessible to a wide range of industries and applications, from healthcare to finance.
For instance, in April 2021, Nvidia Corp. (NVIDIA) announced the launch of a new CPU named after computer scientist Grace Hopper. The Grace CPU is designed specifically for AI and high-performance computing workloads and is expected to be used in conjunction with Nvidias GPUs to create more powerful systems. Thus, the above-mentioned factors are expected to drive the growth of the market in focus during the forecast period.
The high cost of manufacturing is a major challenge impeding the growth of the global deep-learning chips market. They are expensive to manufacture due to several factors, such as the need for special materials and processes. Cost is also driven by the complexity of the design and the high demands for accuracy and precision during manufacturing. One reason for the high cost is the use of advanced materials such as gallium nitride (GaN) and silicon carbide (SiC) required to reduce power consumption and improve performance. These materials are expensive and difficult to process, requiring specialized equipment and skilled personnel, increasing production costs. Another factor is the complexity of the designs, which requires a high level of expertise and precision in production.
As the chips become more sophisticated and complex, the manufacturing processes become more challenging, leading to higher production costs. Moreover, the demand is on the rise, leading to an increase in the cost of materials and equipment used in production. This demand has also led to a shortage of skilled personnel, which further drives up the cost of production. Thus, such factors are expected to hamper the growth of the market in focus during the forecast period.
The 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.: This company focuses on offering deep learning chips such as AMD Instinct MI200 and MI100 series accelerators.
The 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 Million" for the period 2023-2027, as well as historical data from 2017-2021 for the following segments.
Furthermore, the market is witnessing a surge in demand for various types of chips, including artificial intelligence accelerators, edge computing chips, cloud computing chips, data center chips, neuromorphic computing chips, parallel processing chips, and low-power AI chips. These chips are designed to support the growing need for high-performance computing in deep learning applications, driving innovation in the field of AI. The market is witnessing a paradigm shift with the emergence of quantum computing chips, offering unparalleled processing capabilities. This complements the evolution in AI chip architectures, which are becoming more specialized and efficient. AI chip design and AI chip manufacturing are focusing on enhancing performance and reducing power consumption. AI chip Testing and AI chip validation ensure the reliability and functionality of these chips. Various AI chip vendors and AI chip startups are driving innovation in the field, expanding the scope of applications and AI chip use cases across industries. These advancements signify a bright future for AI chip innovations and their impact on technology.
Market Scope |
|
Report Coverage |
Details |
Page number |
179 |
Base year |
2022 |
Historic period |
2017-2021 |
Forecast period |
2023-2027 |
Growth momentum & CAGR |
Accelerate at a CAGR of 34.58% |
Market growth 2023-2027 |
USD 16.52 billion |
Market structure |
Fragmented |
YoY growth 2022-2023(%) |
31.78 |
Regional analysis |
North America, Europe, APAC, South America, and Middle East and Africa |
Performing market contribution |
North America at 35% |
Key countries |
US, China, Taiwan, Germany, and UK |
Competitive landscape |
Leading Vendors, Market Positioning of Vendors, Competitive Strategies, and Industry Risks |
Key companies profiled |
Achronix Semiconductor Corp., Advanced Micro Devices Inc., Alphabet Inc., Amazon.com Inc., Cerebras Systems Inc., China Cambrian Technology Co. Ltd., Flex Logix Technologies Inc., Fujitsu Ltd., Graphcore Ltd., Groq Inc., Intel Corp., International Business Machines Corp., MediaTek Inc., NVIDIA Corp., Qualcomm Inc., Samsung Electronics Co. Ltd., Synopsys Inc., Syntiant Corp., Taiwan Semiconductor Manufacturing Co. Ltd., and ThinkForce |
Market dynamics |
Parent market analysis, Market forecasting, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, Market condition analysis for forecast period. |
Customization purview |
If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
We can help! Our analysts can customize this market research report to meet your requirements.
1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by Technology
7 Market Segmentation by End-user
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Trends
11 Vendor Landscape
12 Vendor Analysis
13 Appendix
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