Increasing Demand for Edge Computing - The growing use of edge computing, where data processing ensues closer to the source of data generation rather than depending solely on centralized cloud servers, fuels the demand for AI chips. Edge devices require efficient and powerful AI processing to handle real-time applications and reduce latency.
Growing Data Volume and Complexity - The explosion of big data and the increasing complexity of data sets drive the need for faster and more efficient processing. AI chips are designed to handle the massive amounts of data involved in training and executing complex machine learning (ML) models.
Rapid Advancements in AI Technologies - The continuous evolution and advancement of AI applications and technologies, including deep learning (DL) and machine learning (ML), are significant drivers. As artificial intelligence algorithms become more complicated, there is a growing demand for specialized hardware acceleration, leading to increased adoption of AI chips.
High Development Costs - The research, development, and manufacturing of advanced AI chips involve substantial costs. High initial investment is a barrier for smaller companies and startups to enter the market, limiting overall competition.
Complexity of Integration - Integrating AI chips into existing systems can be complex, especially in industries with legacy infrastructure. Compatibility issues and the need for system redesign may slow the adoption of AI chips in certain applications.
Increased Adoption in Healthcare - AI chips offer significant opportunities in the healthcare sector, including medical imaging, diagnostics, drug discovery, and personalized medicine. AI in healthcare is expected to drive the demand for specialized chips tailored to medical applications.
Expansion in Autonomous Vehicles- The development and deployment of autonomous vehicles represent a substantial opportunity for AI chip manufacturers. These chips are crucial in processing sensor data and enabling real-time decision-making for safe and efficient autonomous driving.
Edge AI for IoT Devices - The proliferation of IoT devices creates a demand for AI processing at the edge to enhance device intelligence and efficiency. AI chips designed for edge computing open opportunities for applications in smart homes, industrial IoT, and more.
Regulatory and Ethical Concerns - Increasing scrutiny and evolving regulations regarding data privacy, bias, and ethical considerations in AI applications pose challenges. Compliance with diverse global regulations and ensuring ethical use of AI technology are ongoing concerns for the industry.
Global Supply Chain Disruptions - The semiconductor industry, including AI chip manufacturing, is vulnerable to global supply chain disruptions. Events like geopolitical tensions, natural disasters, or pandemics can impact production and lead to shortages.
This study forecasts revenue at global, regional, and country levels from 2020 to 2033. The Brainy Insights has segmented the global Artificial Intelligence Chip market based on below-mentioned segments:
Global Artificial Intelligence Chip Market by Type:
Global Artificial Intelligence Chip Market by Processing Type:
Global Artificial Intelligence Chip Market by Technology:
Global Artificial Intelligence Chip Market by Application:
Global Artificial Intelligence Chip Market by Industry Vertical:
Global Artificial Intelligence Chip Market by Region:
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The phase involves the gathering and collecting of market data and its related information with the help of different sources & research procedures.
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Secondary Research: The secondary data sources includes information published by the on-profit organizations such as World bank, WHO, company fillings, investor presentations, annual reports, national government documents, statistical databases, blogs, articles, white papers and others. From the annual report, we analyse a company’s revenue to understand the key segment and market share of that organization in a particular region. We analyse the company websites and adopt the product mapping technique which is important for deriving the segment revenue. In the product mapping method, we select and categorize the products offered by the companies catering to domain specific market, deduce the product revenue for each of the companies so as to get overall estimation of the market size. We also source data and analyses trends based on information received from supply side and demand side intermediaries in the value chain. The supply side denotes the data gathered from supplier, distributor, wholesaler and the demand side illustrates the data gathered from the end customers for respective market domain.
The supply side for a domain specific market is analysed by:
The demand side for the market is estimated through:
In-house Library: Apart from these third-party sources, we have our in-house library of qualitative and quantitative information. Our in-house database includes market data for various industry and domains. These data are updated on regular basis as per the changing market scenario. Our library includes, historic databases, internal audit reports and archives.
Sometimes there are instances where there is no metadata or raw data available for any domain specific market. For those cases, we use our expertise to forecast and estimate the market size in order to generate comprehensive data sets. Our analyst team adopt a robust research technique in order to produce the estimates:
Data Synthesis: This stage involves the analysis & mapping of all the information obtained from the previous step. It also involves in scrutinizing the data for any discrepancy observed while data gathering related to the market. The data is collected with consideration to the heterogeneity of sources. Robust scientific techniques are in place for synthesizing disparate data sets and provide the essential contextual information that can orient market strategies. The Brainy Insights has extensive experience in data synthesis where the data passes through various stages:
Market Deduction & Formulation: The final stage comprises of assigning data points at appropriate market spaces so as to deduce feasible conclusions. Analyst perspective & subject matter expert based holistic form of market sizing coupled with industry analysis also plays a crucial role in this stage.
This stage involves in finalization of the market size and numbers that we have collected from data integration step. With data interpolation, it is made sure that there is no gap in the market data. Successful trend analysis is done by our analysts using extrapolation techniques, which provide the best possible forecasts for the market.
Data Validation & Market Feedback: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helps us finalize data-points to be used for final calculations.
The Brainy Insights interacts with leading companies and experts of the concerned domain to develop the analyst team’s market understanding and expertise. It improves and substantiates every single data presented in the market reports. The data validation interview and discussion panels are typically composed of the most experienced industry members. The participants include, however, are not limited to:
Moreover, we always validate our data and findings through primary respondents from all the major regions we are working on.
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