Rise of AI in cloud computing and advancements in deep learning technologies are likely to drive the market in the forecast period.
According to TechSci Research report, “Artificial Intelligence Chip Market – Global Industry Size, Share, Trends, Competition Forecast & Opportunities, 2029”, the Global Artificial Intelligence Chip Market is experiencing a surge in demand in the forecast period.
One significant driver propelling the global artificial intelligence chip market is the escalating demand for AI-driven applications across diverse industries. As businesses and consumers increasingly recognize the transformative potential of artificial intelligence, there is a corresponding surge in the need for high-performance AI chips. These chips serve as the computational backbone for a myriad of applications, including machine learning, natural language processing, and computer vision.
Industries such as healthcare, finance, and autonomous vehicles are embracing AI technologies to enhance efficiency, decision-making, and innovation. This expanding adoption necessitates advanced AI chips capable of handling intricate computations with speed and energy efficiency. The growing prevalence of AI in applications ranging from personalized medicine to smart cities is a testament to the vital role AI chips play in driving technological advancements and shaping the future of various sectors.
The proliferation of edge computing stands as a crucial driver influencing the global artificial intelligence chip market. Edge computing involves processing data closer to the source of generation, reducing latency and enhancing real-time decision-making. As the deployment of edge computing gains momentum, there is a parallel demand for AI chips optimized for edge devices.
AI chips designed for edge computing enable the efficient processing of data locally, minimizing the need for data transmission to centralized cloud servers. This is particularly vital for applications such as Internet of Things (IoT) devices, smart sensors, and autonomous systems where low latency is imperative. The convergence of AI and edge computing not only boosts performance but also contributes to the overall growth of the AI chip market, aligning technology with the evolving requirements of decentralized and responsive computing architectures.
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The Global Artificial Intelligence Chip Market is segmented into chip type, processing type, technology, application, end user, and region.
Based on chip type, The GPU segment held the largest Market share in 2023. GPUs are designed with a large number of cores that can perform parallel processing tasks simultaneously. This parallel architecture is highly advantageous for AI workloads, especially deep learning and neural network training, where many calculations can be executed simultaneously. This enables GPUs to handle the massive and parallelizable computations involved in AI applications efficiently.
Deep learning, a subset of machine learning, has become a cornerstone of many AI applications. Deep neural networks have multiple layers, and training them involves numerous matrix operations. GPUs excel in handling these matrix operations in parallel, making them well-suited for accelerating deep learning tasks. This capability has contributed significantly to the dominance of GPUs in AI applications.
GPUs are widely available from various manufacturers, and they have garnered extensive support from the developer community. Major frameworks and libraries used in AI, such as TensorFlow and PyTorch, have GPU acceleration support, making it easier for developers to leverage the parallel processing power of GPUs in their AI applications.
GPUs offer a cost-effective solution for AI tasks compared to other specialized chips like ASICs (Application-Specific Integrated Circuits) in certain scenarios. While ASICs can be highly efficient for specific AI workloads, they are often more expensive to design and manufacture. GPUs, being more general-purpose, provide a cost-effective solution that meets the needs of a broad range of AI applications.
GPUs are versatile and not limited to AI tasks alone. They are widely used in graphics rendering, gaming, and other computational workloads. This versatility makes GPUs attractive for a variety of applications, contributing to their widespread adoption.
Based on application, The Computer Vision segment held the largest Market share in 2023. Computer Vision involves the analysis and interpretation of visual data, such as images and videos. The computational demands of processing vast amounts of visual information are significant. AI chips, particularly GPUs (Graphics Processing Units) and specialized accelerators, excel in handling the parallel processing required for tasks like image recognition, object detection, and scene understanding.
Computer Vision has found applications across various industries, including healthcare (medical imaging), automotive (autonomous vehicles), retail (visual merchandising), surveillance, and more. The broad applicability of Computer Vision has driven the demand for AI chips that can efficiently support a diverse range of visual data processing tasks.
The rise of autonomous systems, such as autonomous vehicles and drones, heavily relies on Computer Vision for navigation, obstacle detection, and decision-making. AI chips play a crucial role in enabling real-time processing of visual data to ensure the safe and efficient operation of these autonomous systems.
Deep learning, a subset of machine learning, has significantly advanced the capabilities of Computer Vision. Convolutional Neural Networks (CNNs) and other deep learning architectures have demonstrated remarkable success in image classification and object detection. AI chips, particularly those optimized for deep learning workloads, are essential for training and inference in these complex models.
The industry's increasing recognition of the potential value offered by Computer Vision has led to substantial investments in research and development. Companies and researchers are actively working on enhancing the performance and efficiency of AI chips for Computer Vision applications, contributing to their dominance in the market.
The proliferation of edge computing, where data is processed closer to the source (e.g., edge devices), has further propelled the demand for AI chips in Computer Vision. Edge devices, including cameras and sensors, require efficient AI processing capabilities for real-time decision-making, making AI chips a critical component in edge computing ecosystems.
Major companies operating in the Global Artificial Intelligence Chip Market are:
- NVIDIA Corporation
- Intel Corporation:
- Qualcomm Technologies Inc.:
- Samsung Electronics Co. Ltd.:.
- Huawei Technologies Co. Ltd.
- MediaTek Inc
- Micron Technology, Inc.
- NXP Semiconductors N.V.
- Advanced Micro Devices Inc. (AMD):
- Google LLC:
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“The Global Artificial Intelligence Chip Market is expected to rise in the upcoming years and register a significant CAGR during the forecast period. The primary driver behind the global artificial intelligence chip market is the burgeoning demand for heightened AI performance in business applications. As industries increasingly integrate AI technologies for improved decision-making and efficiency, the necessity for advanced AI chips intensifies. These chips serve as the computational engines powering machine learning, natural language processing, and computer vision.
In meeting the escalating demand for faster and more energy-efficient AI computations, businesses are driving the innovation and growth of the AI chip market. The imperative for superior AI performance underscores the pivotal role these chips play in optimizing processes and fueling the ongoing technological evolution across sectors. Therefore, the Market of Artificial Intelligence Chip is expected to boost in the upcoming years.,” said Mr. Karan Chechi, Research Director with TechSci Research, a research-based management consulting firm.
“Artificial Intelligence Chip Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2019-2029 Segmented By Chip Type (GPU, ASIC, FPGA, CPU, Others), By Processing Type (Edge, Cloud), By Technology (System On Chip, System in Package, Multi Chip Module, Others), By Application (Nature Language Processing, Robotics, Computer Vision, Network Security, Others), By End User (Media and Advertising, BFSI, IT and Telecom, Retail, Healthcare, Automotive and Transportation, Others), By Region, By Competition”, has evaluated the future growth potential of Global Artificial Intelligence Chip Market and provides statistics & information on Market size, structure and future Market growth. The report intends to provide cutting-edge Market intelligence and help decision-makers make sound investment decisions., The report also identifies and analyzes the emerging trends along with essential drivers, challenges, and opportunities in the Global Artificial Intelligence Chip Market.
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