The key players operating in the Artificial Intelligence (chipsets) Market are AI (chipsets) market is expected to be valued at USD 18.6 billion in 2023 and is projected to reach USD 64.5 billion by 2028; it is expected to grow at a compound annual growth rate (CAGR) of 28.1% from 2023 to 2028. , AI has become a transformative technology across various industries. AI (chipsets) is purpose-built to deliver high-speed processing, low latency, and parallel computing capabilities, enabling faster and more responsive AI applications. Organizations are leveraging AI to automate processes, gain valuable insights from data, improve decision-making, enhance customer experiences, and drive innovation. As the adoption of AI continues to expand, there is a growing demand for specialized hardware that can efficiently process AI workloads, leading to the growth of the AI (chipsets) Industry. , Artificial Intelligence (chipsets) market dynamics , Driver: The emerging trend of autonomous vehicles , Autonomous vehicles rely on a combination of sensors, cameras, radar, lidar, and other technologies to perceive their surroundings accurately. AI (chipsets) plays a crucial role in processing the vast amount of real-time data generated by these sensors. The chipsets accelerate perception tasks such as object detection, tracking, and classification, allowing the vehicle to make informed decisions based on the analyzed data. The need for powerful AI (chipsets) capable of handling complex perception tasks is essential to enable safe and efficient autonomous driving. , Autonomous vehicles employ sophisticated AI algorithms for mapping, path planning, and decision-making tasks. These algorithms require substantial computational power and efficient processing to handle driving lessons' complexity and real-time nature. AI (chipsets) is designed to deliver the high-performance computing needed to execute these complex algorithms efficiently, ensuring the smooth operation of autonomous vehicles. , Restraint: Lack of AI hardware experts and skilled workforce , Developing AI (chipsets) requires specialized knowledge and expertise in hardware design, architecture, and optimization for AI workloads. However, there is a need for more AI hardware experts who possess the necessary skills and experience to design and develop these chipsets. This expertise scarcity can slow the pace of innovation and product development in the AI (chipsets) market. , AI (chipsets) often incorporates specialized accelerators and custom architectures tailored for AI workloads. Designing and optimizing these components requires technical skills and knowledge that may be limited in the existing talent pool. The need for more skilled workers who can handle these specialized tasks can restrict the growth and development of AI (chipsets). , Opportunity: Surging demand for AI-based FPGA , FPGAs offer inherent flexibility and programmability compared to fixed-function ASICs (Application-Specific Integrated Circuits). This makes them suitable for handling diverse AI workloads and adapting to evolving AI algorithms. As AI models and algorithms continue to grow rapidly, the ability to reprogram and reconfigure FPGAs provides a competitive advantage in meeting the changing demands of AI applications. , Energy efficiency is critical in AI (chipsets), particularly in edge computing and IoT devices where power constraints exist. FPGAs can be power-optimized to deliver high performance per watt by leveraging parallel processing capabilities and fine-grained control over resources. The ability to optimize power consumption while maintaining performance is crucial for AI (chipsets), making AI-based FPGAs an attractive choice. , Challenge: Data privacy concerns in AI platforms , AI platforms often require access to large datasets, including personal and sensitive information. This raises concerns about data security and protection. If the data used for training AI models is not adequately safeguarded, it can be vulnerable to unauthorized access, breaches, or misuse. This can lead to privacy violations, identity theft, or other forms of data abuse. , AI platforms often involve the sharing of data across organizations or even international borders. However, data privacy regulations can vary across jurisdictions, making it challenging to ensure compliance and protect user privacy. Adhering to diverse legal frameworks while enabling data sharing and collaboration poses a significant challenge for AI (chipsets) companies. , Artificial Intelligence (chipsets) Market Ecosystem , The Artificial Intelligence (chipsets) market is dominated by established and financially sound manufacturers with extensive experience in the industry. These companies have diversified product portfolios, cutting-edge technologies, and strong sales and marketing networks. Leading players in the market include Intel Corporation from the US, Nvidia Corporation from the US, Qualcomm Technologies Incorporated from the US, Advanced Micro Devices, Inc. from the US, and Alphabet Inc. from the US. , Based on technology, the Artificial Intelligence (chipsets) market for Natural Language Processing to hold the second highest CAGR during the forecast period , The exponential growth of digital data, including unstructured data like text, presents both challenges and opportunities. NLP technology combined with AI (chipsets) can efficiently analyze and extract insights from vast amounts of textual data, enabling businesses to uncover valuable information, trends, and patterns hidden within text documents. Powered by AI (chipsets), NLP technology automates language-intensive tasks previously performed manually. This includes functions like document summarization, information extraction, sentiment analysis, and content generation. Automating these tasks improves efficiency, reduces errors, and frees up human resources for more complex and strategic work. , Based on hardware, the processor segment to hold the highest market share from 2023 to 2028 , AI applications often require high-performance computing capabilities to handle massive amounts of data and complex computations. General-purpose processors, such as CPUs (Central Processing Units), may not be optimized for the specific requirements of AI workloads. Specialized processors, such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Neural Processing Units (NPUs), are designed to deliver parallel processing and optimized performance for AI tasks. The growing demand for high-performance computing in AI applications is driving the growth of the AI (chipsets) processor market. , Training function for Artificial Intelligence (chipsets) market to grow at the highest CAGR during the forecast period. , The demand for AI-driven applications and services is rising across various industries, such as healthcare, finance, e-commerce, autonomous vehicles, and more. Training AI models requires substantial computational power, and AI (chipsets) provide the specialized hardware needed to accelerate and optimize the training process. , Cybersecurity industry for Artificial Intelligence (chipsets) market to hold the second largest market share from 2023 to 2028 , Cybersecurity threats are becoming more sophisticated and complex, requiring advanced technologies to detect and mitigate them effectively. AI (chipsets) offers the computational power and efficiency needed to analyze large volumes of data, identify patterns, and detect anomalies in real-time, helping to enhance cybersecurity defenses. AI (chipsets) enables automation in cybersecurity processes, such as malware detection, intrusion detection, and log analysis. By automating these tasks, AI-powered cybersecurity systems can free up human resources, reduce response times, and improve efficiency in identifying and mitigating threats. , Artificial Intelligence (chipsets) market in Asia Pacific to hold the highest CAGR during the forecast period , Asia Pacific is witnessing a rapid digital transformation across various sectors, including healthcare, finance, manufacturing, retail, and transportation. This transformation is driving the adoption of AI technologies, leading to an increased demand for AI (chipsets) to power AI applications and services. , Asia Pacific has a vibrant AI startup ecosystem, with emerging companies focused on developing AI applications and technologies. These startups are driving the demand for AI (chipsets), seeking high-performance and energy-efficient hardware solutions to power their AI innovations. , Various industries in Asia Pacific, including healthcare, finance, automotive, retail, and agriculture, are increasingly adopting AI technologies to improve operational efficiency, enhance customer experiences, and drive innovation. AI (chipsets) is critical in powering AI applications in these industries, contributing to the market’s growth. , Recent Developments , In November 2022, Nvidia announced a collaboration with Microsoft. As a part of this collaboration, companies will build one of the most powerful AI supercomputers in the world, powered by Microsoft Azure’s advanced supercomputing infrastructure combined with NVIDIA GPUs, networking, and a whole stack of AI software to help enterprises train, deploy and scale AI, including oversized, state-of-the-art models. , In October 2022, Intel and HashiCorp joined forces to aid customers in enhancing their cloud migration efforts. By leveraging Intel's Xeon Scalable accelerators, developers will receive Sentinel policy recommendations from HashiCorp's products, enabling them to optimize workloads and maximize their cloud strategy's cost-effectiveness, performance, and security. , In October 2022, NSF announced a partnership with Micron to support semiconductor design and manufacturing workforce development. , In October 2022, Samsung announced the launch of its latest LPDDR5X DRAM with the industry’s fastest speed of 8.5 gigabits per second. , In April 2022, AMD signed a definitive agreement to acquire Pensando Systems Inc. (US) to add chips and software to route information inside computer systems and expand its data center solutions capabilities. , KEY MARKET SEGMENTS , By technology , Machine Learning , Natural Language Processing , Context-Aware Computing , Computer Vision , Predictive Analysis , By function , Training , Inference , By hardware , Processor , Memory , Network , By end-user , Healthcare , Manufacturing , Automotive , Agriculture , Retail , Cybersecurity , Human Resources , Marketing , Law , Fintech , Government , By Region , North America , Europe , Asia Pacific , RoW , KEY MARKET PLAYERS , Intel Corporation , Nvidia Corporation , Qualcomm Technologies Incorporation , Micron Technology, Inc. , Advanced Micro Devices, Inc..
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