Global Machine Learning Chip Market Size, Share Analysis, Trends, Report 2023-2028
Global Machine Learning Chip Market Overview 2023-2028
IMARC Group, a leading market research company, has recently releases report titled “Machine Learning Chip Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023-2028.” The study provides a detailed analysis of the industry, including the global machine learning chip market share, size, trends, and growth forecasts. The report also includes competitor and regional analysis and highlights the latest advancements in the market.
The global machine learning chip market size reached US$ 7.8 Billion in 2022. Looking forward, IMARC Group expects the market to reach US$ 35.0 Billion by 2028, exhibiting a growth rate (CAGR) of 25.4% during 2023-2028.
Machine learning (ML) chips are semiconductor devices engineered to perform complex mathematical computations and data processing tasks essential for machine learning (ML) and artificial intelligence (AI) algorithms. They are purpose-built to excel at specific types of computations that are prevalent in deep learning and other AI applications. They are integral to the advancement of AI and are utilized in a wide range of applications, ranging from smartphones and data centers to autonomous vehicles and edge devices. They are designed to deliver exceptional speed and energy efficiency when executing AI workloads. They are well-suited for edge computing scenarios, where data processing occurs locally on devices rather than in centralized data centers, reducing latency and enabling devices like smartphones and Internet of Things (IoT) sensors to make AI-driven decisions without relying on cloud connectivity. They can process large volumes of data and perform complex calculations at a much faster rate as compared to general-purpose processors, resulting in quicker AI model training and inference. They assist in reducing power consumption as compared to central processing units (CPUs) or graphics processing units (GPUs), which is crucial for mobile devices and edge computing, where energy efficiency is paramount. They excel at parallel processing, which is fundamental to training deep neural networks, as their architecture allows for concurrent execution of numerous calculations, resulting in improved model accuracy and faster training times. They enable rapid inference, ensuring quick responses to changing environmental conditions in applications requiring real-time decision-making, such as autonomous vehicles and industrial automation. They can be employed in a scalable manner, making them suitable for a wide range of applications, such as running ML algorithms on a mobile device or a massive data center.
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Global Machine Learning Chip Market Trends and Drivers:
At present, the rising demand for AI applications across various industries represents one of the key factors supporting the growth of the market. AI is being utilized in fields, such as healthcare, finance, automotive, and manufacturing, to improve efficiency, make data-driven decisions, and enhance user experiences. ML chips play a crucial role in accelerating AI tasks, making them essential for the widespread adoption of AI technologies. Besides this, edge computing, which involves processing data closer to the source rather than relying solely on centralized data centers, is gaining traction. ML chips are well-suited for edge devices, enabling real-time inference and decision-making in applications like autonomous vehicles, IoT devices, and smartphones. As edge computing is expanding, the demand for ML chips is increasing across the globe. Moreover, deep learning is leading to more complex and capable AI models. ML chips are designed to handle the intensive computational requirements of deep learning algorithms, driving their adoption in applications like natural language processing (NLP), computer vision, and speech recognition, which is strengthening the growth of the market. In addition, ML chips are engineered to optimize power consumption while delivering high performance for AI workloads. This energy-efficient design is particularly important in mobile devices, where prolonged battery life is paramount.
Global Machine Learning Chip Market 2023-2028 Analysis and Segmentation:
Top Key Players covered in this report are:
Advanced Micro Devices Inc., Amazon Web Services Inc. (Amazon.com Inc.), Cerebras Inc., Google LLC, Graphcore, Intel Corporation, International Business Machines Corporation, NVIDIA Corporation, Qualcomm Incorporated, Samsung Electronics Co. Ltd. and Taiwan Semiconductor Manufacturing Company Limited
The report segmented the market on the basis of region, technology, chip type and industry vertical.
Technology Insights:
- System-on-Chip (SoC)
- System-in-Package
- Multi-chip Module
- Others
Chip Type Insights:
- GPU
- ASIC
- FPGA
- CPU
- Others
Regional Insights:
- North America: (United States, Canada)
- Asia Pacific: (China, Japan, India, South Korea, Australia, Indonesia, Others)
- Europe: (Germany, France, United Kingdom, Italy, Spain, Russia, Others)
- Latin America: (Brazil, Mexico, Others)
- Middle East and Africa
Key highlights of the report:
- Market Performance (2017-2022)
- Market Outlook (2023- 2028)
- Porter’s Five Forces Analysis
- Market Drivers and Success Factors
- SWOT Analysis
- Value Chain
- Comprehensive Mapping of the Competitive Landscape
If you need specific information that is not currently within the scope of the report, we can provide it to you as a part of the customization.
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