More than 83 Per cent of Global Chipsets Will be AI-equipped by 2025: Report

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With 83 per cent of all chipsets globally shipping AI-equipped, over 57 per cent of all electronics will have some form of embedded intelligence by 2025

The AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. With 83 per cent of all chipsets globally shipping AI-equipped, over 57 per cent of all electronics will have some form of embedded intelligence by 2025. This will also be transformational for existing critical business functions such as identity management, authentication, and cybersecurity.

“Consumers will realize benefits indirectly through improved product and service performance such as device and cloud-based gaming. Enterprise and industrial users will benefit through general improvements in automated decision-making, especially in the areas of robotic process automation, decision support systems, and overall data management,” read a latest report by Research and Markets.

It continued, “AI chipsets will be particularly useful for business edge equipment for real-time data analytics and store versus processing decisions.”

AI chipsets will become an integral part

Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden patterns among data, predict actionable insights and perform actions based on specific situations. AI chipsets, as per the report, will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance.

This will be a boon for many aspects of ICT ranging from decision support and data analytics to product safety and system optimization. Another report by Markets and Markets has forecast the AI Chipset Market to reach 57.8 billion by 2026.

Major drivers for the market are increasingly large and complex datasets driving the need for AI, the adoption of AI for improving consumer services and reducing operational costs, the growing number of AI applications, the improving computing power, and growing adoption of deep learning and neural networks.

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