Newark, Jan. 11, 2024 (GLOBE NEWSWIRE) -- The Brainy Insights estimates that the USD 14.68 billion in 2022 global Artificial Intelligence Chip market will reach USD 372.01 billion by 2032. The intersection of AI and quantum computing is an area of increasing interest. Developing AI chips that can leverage the capabilities of quantum computing for enhanced processing power and solving complex AI problems represents a potential future opportunity. Furthermore, advancements in NLP, driven by the development of large language models and conversational AI, present opportunities for specialized AI chips optimized for natural language understanding and processing. Additionally, with the rise of cyber threats, AI-driven cybersecurity applications are gaining prominence. AI chips designed for efficient threat detection, anomaly analysis, and network security could see increased demand in the coming years. As AI systems become more prevalent, there is a rising focus on ethical AI practices and the need for transparency. AI chips that enable explainable AI, ensuring clear and understandable decision-making processes, may become essential in certain applications. In addition, the demand for energy-efficient AI chips, often called "Green AI," is likely to grow as sustainability becomes a key consideration. Developing chips that balance performance with low power consumption aligns with the trend toward eco-friendly AI solutions.
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Key Insight of the global Artificial Intelligence Chip market
Asia Pacific is expected to witness the highest market growth over the forecast period.
The Asia-Pacific region, particularly governments like China and India, has experienced rapid economic growth. The expanding middle class and increasing urbanization contribute to a growing consumer base, creating significant market potential for AI applications and, consequently, AI chips. In addition, governments in the Asia-Pacific region have recognized the strategic importance of AI in driving economic development and innovation. Many countries have introduced policies and initiatives to support AI research, development, and adoption, including investments in semiconductor and AI chip technologies. Asia-Pacific is a key hub for semiconductor manufacturing, with countries like Taiwan and South Korea hosting major semiconductor companies. The region's established manufacturing capabilities and supply chain strength position it as a crucial player in the production and distribution of AI chips. Furthermore, the Asia-Pacific region has witnessed the emergence of a vibrant technology ecosystem, including established tech companies and a flourishing startup culture. This ecosystem fosters innovation and collaboration in AI chip development, contributing to the region's anticipated growth. Moreover, industries across Asia-Pacific, including manufacturing, healthcare, finance, and telecommunications, are increasingly adopting AI technologies. The demand for AI chips to power these applications, such as smart manufacturing and healthcare diagnostics, is expected to drive growth in the AI chip market.
In 2022, the central processing unit (CPU) segment held the largest market share at 31.25% and a market revenue of 4.59 billion.
The type segment is divided into application-specific integrated circuit (ASIC), central processing unit (CPU), field programmable gate array (FPGA), graphics processing unit (GPU) and others. In 2022, the central processing unit (CPU) segment held the largest market share at 31.25% and a market revenue of 4.59 billion.
In 2022, the edge segment dominated the market with the largest share of 74.82%and revenue of 10.99 billion.
The processing type segment includes cloud and edge. In 2022, the edge segment dominated the market with the largest share of 74.82% and revenue of 10.99 billion.
In 2022, the system on chip segment dominated the market with the largest share of 37.29% and revenue of 5.47 billion.
The technology segment is classified into system on chip, system in package, multi-chip module and others. In 2022, the system on chip segment dominated the market with the largest share of 37.29% and revenue of 5.47 billion.
In 2022, the nature language processing segment dominated the market with the largest share of 30.17% and revenue of 4.43 billion.
The application segment is split into computer vision, nature language processing, network security, robotics and others. In 2022, the nature language processing segment dominated the market with the largest share of 30.17% and revenue of 4.43 billion.
In 2022, the BFSI segment dominated the market with the largest share of 22.16% and revenue of 3.25 billion.
The industry vertical segment is divided into automotive and transportation, BFSI, healthcare, it and telecom, media and advertising, retail and others. In 2022, the BFSI segment dominated the market with the largest share of 22.16% and revenue of 3.25 billion.
Advancement in market
In November 2023: Microsoft has developed proprietary AI chips tailored for training extensive language models, a strategic move to reduce dependency on Nvidia and potential cost implications. In addition, Microsoft has also created its Arm-based CPU specifically for cloud workloads. These custom silicon chips are meticulously crafted to drive the capabilities of Azure data centers, positioning the company and its enterprise clients for an AI-driven future. The Microsoft Azure Maia 100 AI Accelerator and the Microsoft Azure Cobalt CPU are engineered to enhance infrastructure performance, particularly in training large language models.
In October 2023: Infineon Technologies AG, a renowned global semiconductor manufacturer specializing in power systems and IoT, has entered into a strategic partnership with Archetype AI, Inc., an innovator in AI solutions for the physical realm. The collaboration aims to expedite the advancement of sensor-based chips embedded with AI capabilities, contributing to a future where daily life becomes more convenient, secure, and environmentally sustainable. Infineon will pioneer using the LBM AI developer platform in this multi-year partnership. This platform empowers the generation of AI agents that autonomously generate code tailored for customer-specific sensor applications. These generated codes are designed to function as edge models on customer devices, enhancing sensor technology's overall efficiency and versatility.
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Market Dynamics
Driver: Increased investment in AI research and development.
Many governments worldwide have recognized the strategic importance of AI and are actively investing in research and development. These initiatives often involve funding for academic institutions and research centers and collaborations with industry to foster innovation in AI and AI chip technologies. Public sector organizations, including government agencies, also invest in AI for various applications, such as healthcare, defence, transportation, and public services. These investments contribute to developing and deploying AI chips tailored to meet the specific needs of the public sector. Furthermore, leading technology companies, often called tech giants, heavily invest in AI research and development. These companies, including Google, Microsoft, Apple, and others, allocate significant resources to advance AI chip technologies for use in their products and broader industry advancements.
Restraint: Data security and privacy issues.
Commitment to data protection and privacy regulations is crucial. Organizations deploying AI solutions, including those powered by AI chips, must navigate complex regulatory landscapes, such as the GDPR in the European Union. Non-compliance can lead to lawful consequences and harm to reputation. In the healthcare sector, patient data is highly sensitive and subject to stringent privacy regulations, such as the HIPAA in the United States. Adopting AI chips for applications like medical diagnosis, patient monitoring, and personalized medicine raises concerns about safeguarding patient information against unauthorized access or breaches. Additionally, privacy concerns arise when AI applications involve processing personal information. Using AI chips in applications like facial recognition, voice recognition, and behavioural analysis may trigger concerns about collecting, storing, and potentially misusing individuals' private data.
Opportunity: Global expansion of AI in education.
Flexible learning platforms use AI algorithms to personalize and customize educational content based on individual student needs, learning styles, and performance. AI chips play a crucial role in accelerating the processing of complex algorithms, enabling real-time adaptation and customization of learning materials for each student. Furthermore, intelligent tutoring systems use AI to provide personalized guidance and student feedback, simulating a one-on-one tutoring experience. AI chips enhance the performance of these systems by efficiently processing large datasets, assessing student progress, and dynamically adjusting instructional strategies. Additionally, AI chips optimized for NLP are essential for language understanding and generation applications. In educational settings, NLP-enabled AI chips support the development of intelligent conversational agents, chatbots, and virtual tutors that can engage with students in natural language, answer questions, and provide assistance.
Challenge: Limited skilled workforce.
The need for more skilled professionals with expertise in AI chip design and optimization is a significant challenge the industry faces. The requirement for individuals with specialized skills in this field has grown rapidly, but the supply of qualified professionals has not kept pace. This shortage can hinder the development of innovative solutions and the broader adoption of AI chip technologies. The shortage is wider than in specific geographic regions. It is a global challenge. Companies worldwide compete for a limited pool of skilled professionals, creating a global talent competition that makes it challenging for some organizations to attract top talent.
Some of the major players operating in the global Artificial Intelligence Chip market are:
• Advanced Micro Devices Inc.
• Amazon
• General Vision Inc.
• Google Inc.
• GRAPHCORE Ltd.
• GROQ
• Gyrfalcon Technology Inc.
• Huawei Technologies Co. Ltd.
• Infineon Technologies AG
• IBM Corporation
• INTEL Corporation
• KnuEdge, Inc.
• KRTKL Inc.
• MEDIATEK, Inc.
• Microsoft Corporation
• Micron Technology, Inc.
• Microsemi Corporation
• Microchip Technology Inc.
• MYTHIC, Inc.
• NEC Corporation
• NVIDIA Corporation
• VIDIA Corporation
• NXP Semiconductors N.V.
• Qualcomm Incorporated
• Samsung Electronics Co. Ltd.
• SK Hynix, Inc.
• Tenstorrent Inc.
• Texas Instruments Incorporated
• Toshiba Corporation
• Wave Computing, Inc.
• Xilinx, Inc.
Key Segments cover in the market:
By Type
• Application-Specific Integrated Circuit (ASIC)
• Central Processing Unit (CPU)
• Field Programmable Gate Array (FPGA)
• Graphics Processing Unit (GPU)
• Others
By Processing Type
• Cloud
• Edge
By Technology
• System on Chip
• System in Package
• Multi-Chip Module
• Others
By Application
• Computer Vision
• Nature Language Processing
• Network Security
• Robotics
• Others
By Industry Vertical
• Automotive and Transportation
• BFSI
• Healthcare
• IT and Telecom
• Media and Advertising
• Retail
• Others
By Region
• North America (U.S., Canada, Mexico)
• Europe (Germany, France, the UK, Italy, Spain, Rest of Europe)
• Asia-Pacific (China, Japan, India, Rest of APAC)
• South America (Brazil and the Rest of South America)
• The Middle East and Africa (UAE, South Africa, Rest of MEA)
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About the report:
The market is analyzed based on value (USD Billion). All the segments have been analyzed worldwide, regional, and country basis. The study includes the analysis of more than 30 countries for each part. The report analyses driving factors, opportunities, restraints, and challenges to gain critical market insight. The study includes Porter's five forces model, attractiveness analysis, Product analysis, supply, and demand analysis, competitor position grid analysis, distribution, and marketing channels analysis.
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