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The artificial intelligence chips market size is projected to increase by USD 389.25 billion, at a CAGR of 68.13% between 2023 and 2028. The growth of the market depends on several factors, including increasing adoption of AI chips in data centers, increased focus on developing AI chips for smartphones, and development of AI chips in autonomous vehicles. AI chips are used in processing units in AI applications such as autonomous vehicles, robotics, and data analysis in data centers. These are integrated into applications that require hallmarks of human intelligence, such as the ability to learn, reason, and learn from past experiences. The development involves applying the characteristics of human intellect in building computer algorithms.
The market growth and forecasting report includes key player's detailed analyses of the competitive landscape of the market and information about key companies, including 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., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.
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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.
Market analysis and report of qualitative and quantitative analysis of companies 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 companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
The market share growth by the ASICs segment will be significant during the forecast period. ASICs are specialized, non-configurable chips that are highly customized. ASICs are programmable integrated circuits (ICs) that resemble GPUs in appearance but provide an instruction set and libraries that enable the IC to operate on locally stored data and serve as an accelerator for numerous parallel algorithms. Moreover, ASICs perform faster than GPUs and FPGAs but cannot be reconfigured.
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The ASICs segment was valued at USD 4.36 billion in 2018. The segment is being driven by the use of ASICs in cloud-based data centers. ASIC-based AI chips are expanding their market share as data center applications favor them over GPUs and FPGAs. When compared to GPUs, FPGAs, and CPUs, these chips deliver greater performance and speed. Further, applications like Google Street View and Google Search already make use of it. To effectively manage the data, many data centers are, therefore, integrating TPUs at the back end of servers. These TPUs have a set of instructions that can be used to modify TensorFlow programs and create new algorithms. Tensor Processing Units (TPU) v4 was successfully introduced by Google in May 2021. Compared to TPU v3 hardware, TPU v4 will double TPU hardware performance. On the Google cloud platform, the new version will speed up machine learning training. Microsoft and Amazon.com are also working on TPUs and are planning to implement them in their cloud offerings. Therefore, AI chips based on ASICs are expected to witness significant growth during the forecast period.
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North America is estimated to contribute 51% to the growth of the global market during the forecast period.
Technavio's analysts have provided an extensive insight into the market forecast, detailing the regional trends and drivers influencing the market's trajectory throughout the forecast period. The North American market for artificial intelligence chips is experiencing significant growth, primarily driven by increased investments in autonomous vehicles. These vehicles incorporate advanced systems such as ADAS, HUD, LiDAR, and RADAR. Electronic components like sensors, microcontrollers, microprocessors, and RF components play a crucial role in generating and processing real-time data. Consequently, chip manufacturers are investing heavily in R&D to develop these designed for autonomous vehicles.North America is home to several top automakers, including Ford, GM, Chrysler, and Porsche. Luxury and sports car production is prominent in Canada, the US, and Mexico. The region has witnessed substantial investments in facility construction and capacity expansion.
The artificial intelligence (AI) chips market is rapidly advancing with breakthroughs in quantum computing, generative AI, and the development of supercomputers equipped with highbandwidth memory and cutting-edge Trainium2 chips. These innovations cater to diverse industries including retail, finance, and the proliferation of IoT devices. However, alongside these advancements, there are growing ethical concerns surrounding AI applications and specific integrated technologies like CPU, FPGA, GPU, and system on chip solutions. The trend towards edge computing and multichip module architectures further underscores the market's expansion and evolution towards more efficient and powerful AI processing capabilities. As demand increases for AI-driven solutions, competition among manufacturers intensifies, driving continual innovation and adaptation to meet the complex computational needs of modern AI applications.
Increased focus on developing AI chips for smartphones is a key driver propelling market growth. Many smartphone OEMs are integrating dual-lens cameras, triple-lens cameras, augmented reality (AR), facial recognition, and several other innovative technologies to attract customers to upgrade their devices. The integration of these technologies requires these chips that can process data faster. A majority of the smartphones available in the market have the above-mentioned technologies, but these applications are not efficiently supported by the processors integrated into these devices. This limitation highlights the need for advancements in technology, including robotics, hardware components, and AI algorithms, to better support machine learning (ML) applications.
Moreover, the EPYC processor series and other AI-based tools are being developed to enhance the performance of smartphones and other devices, focusing on integrated circuits, algorithmic calculations, and neural network architectures. Therefore, smartphone users are finding these technologies a valuable addition to their devices. Therefore, smartphone OEMs have started investing heavily in the development that offers the potential for use in the advanced technologies deployed in smartphones. These factors are going to drive the growth of the market in focus during the forecast period.
Increasing investments in AI start-ups is an emerging trend influencing the growth of the market. Since this technology is still in its infant stage, its adoption is expanding rapidly across many industries. As a result, several businesses, including start-ups, are entering the market to benefit from the rising demand for AI technology. Approximately 70%-75% of businesses are predicted to have adopted AI technology in some capacity within the next 20 years.
Furthermore, due to the enormous market potential, major chip manufacturers and venture capitalists have been making sizeable investments in several start-up companies for the creation of AI platforms and chipsets. For example, Shanghai-based startup Innostar Semiconductor raised USD 100 million funding in a pre-series A round of funding in 2021. This was led by Shanghai Lianhe Investment and joined by New Alliance as well as new investors Atlas Capital and KQ Capital. The money will be used by the business to create chips for resistive RAM and storage. Hence, these factors will drive the growth of the market during the forecast period.
Complexities in understanding data are major challenges impeding artificial intelligence chips market growth. Language, context, and reasoning are three obstacles that AI must get past to understand data at its best. Currently used AI technologies face challenges processing information in the same way that humans do. This is because the majority of information is processed as data rather than as language. Since they interpret the data as spatial text, contextual issues arise. However, natural language must be processed in the appropriate context, which can only be created when the language is understood rather than just being read.
Additionally, to make decisions, AI technologies must process the data and arrive at the proper understanding. This technology cannot solve complex problems without the ability to understand. This poses a significant challenge for the market because numerous AI systems must analyze enormous amounts of data that are processed continuously. The market will continue to observe limited growth unless highly efficient systems are created to overcome the complexities involved in understanding data. Therefore, such factors will hinder the artificial intelligence chips market growth during the forecast period.
The market forecasting report includes the adoption lifecycle of the market research and growth, 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 market growth and trends strategies.
Global Market Customer Landscape
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.
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The artificial intelligence (AI) chips market is undergoing a transformative phase with the introduction of advanced technologies like the Trainium2 chip and Nvidia's A100 chip. These chips cater to diverse needs in energy efficiency and AI chip lines, competing with offerings such as the Ascend 910B and H200 chipsets. Major cloud providers like Microsoft Azure, Amazon Web Services, and Oracle Cloud Infrastructure leverage these innovations for data processing in centralized cloud servers and Edge devices for real-time applications. These developments address challenges like latency and handle vast amounts of big data through high computing and parallel computing capabilities. AI data centers play a crucial role in cognitive computing and machine intelligence, supporting applications ranging from image recognition to health monitoring and personalized health.
Further, the market's expansion into mobile applications, smart homes, and manufacturing machines underscores its broad impact across industries, driving advancements in DSP, frame buffer, and programmable logic chip technologies for enhanced visual understanding and automatic analysis. The market is driven by the demand for AI algorithms and machine learning (ML) applications in various industries. These are integrated circuits designed for algorithmic calculations and optimized for neural network architectures. These chips enable deep learning and other functions, making them essential for industries like autonomous vehicles, healthcare, and robotics. Leading companies like EPYC processor series are developing AI-based tools to enhance computer vision and other capabilities, driving innovation in the tech industry through advanced memory structures and general-purpose processors.
Industry Scope |
|
Report Coverage |
Details |
Page number |
188 |
Base year |
2023 |
Historic period |
2018 - 2022 |
Forecast period |
2024-2028 |
Growth momentum & CAGR |
Accelerate at a CAGR of 68.13% |
Market growth 2024-2028 |
USD 389.25 billion |
Market structure |
Fragmented |
YoY growth 2023-2024(%) |
53.8 |
Regional analysis |
North America, Europe, APAC, South America, and Middle East and Africa |
Performing market contribution |
North America at 51% |
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 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 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 Product
7 Market Segmentation by End-user
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Opportunity/Restraints
11 Competitive Landscape
12 Competitive Analysis
13 Appendix
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