Artificial Intelligence

The role of semiconductors for Artificial Intelligence

Discover the role of semiconductors in Artificial Intelligence (AI) and how they enable high-performance computing, efficient power management, and secure data processing. Learn about the different types of semiconductors used in AI and how Infineon is driving the AI revolution.

Internet of Things
Edge AI
Article

Jul 10, 2025

Artificial Intelligence – the next technological revolution

Artificial Intelligence (AI) is the key technology of the digital age and will be crucial for many devices in the future. Whether it's automated driving, smart home devices, facial recognition, music streaming, voice translation, medical diagnoses, navigation, service and industrial robots - artificial intelligence is increasingly changing the way we live, work, and communicate and we are already taking huge advantage of it.

But what makes AI possible? The answer: it’s semiconductors.

Smart health appliances microchip
Smart health appliances microchip
Smart health appliances microchip

Innovative semiconductors – The heart of Artificial Intelligence

Semiconductors are the building blocks of AI systems, enabling the high-performance computing required for machine learning, deep learning, generative AI and other AI-related tasks. Our focus at Infineon is to consider the entire ecosystem necessary to support AI functionalities. To enable AI systems many types of semiconductors play a crucial role, such as:

  • Processors: These "brains" of AI systems execute instructions, perform calculations, and controle data flow. While Graphics Processing Units (GPUs) are specifically designed to handle the complex calculations required for AI and machine learning, Central Processing Units (CPUs) are designed for serial processing and are responsible for executing the instructions that control the AI system.
  • AI Sensors: As the starting point of an AI system sensors collect and generate data for AI algorithms to work with. AI sensors include image sensors for object detection, facial recognition, and autonomous vehicles, audio sensors that are used in audio devices to capture sound data, and environmental sensors, that measure temperature, humidity, pressure, and other environmental factors.
  • Actuators: Power semiconductors in AI systems like Power Supply Units (PSUs), Battery Backup Units (BBUs) and Intermediate Bus Converters (IBCs) are used to manage power consumption in AI systems, ensuring that the system receives the power it needs to operate and to control motors and other actuators in AI systems, such as robots and autonomous vehicles.
  • Connectivity solutions: Wireless Connectivity ICs (Bluetooth, UWB), or ethernet ICs enable communication with a cloud or between AI systems and other devices, such as peripherals and accessories.
  • Security semiconductors: Security solutions create trust in AI systems by protecting sensitive data from unauthorized access, providing a secured environment for AI systems and for encrypting and decrypting data in AI systems.
  • Memory and Storage: Volatile and non-volatile memory and storage solutions reliably retrieve vast amounts of data for AI models.

We power AI

The “grid to core” approach

All processors, in which the AI algorithms are trained and executed, run in special data centers. The exponential data growth driven by digitalization and artificial intelligence is significantly increasing the energy and technology requirements of data centers.

Smart health appliances microchip
Smart health appliances microchip
Smart health appliances microchip

Power semiconductors ensure that AI data centers operate efficiently, managing the energy flow from the power grid all the way to the processors, the core, which is critical as the energy demands of these systems increase rapidly due to growing data processing needs.

We at Infineon emphasize a 'grid to core' approach in powering AI. The 'grid to core' approach is a comprehensive strategy that Infineon has developed to address the power needs of AI from the very beginning of the energy supply chain—starting at the grid—right down to the core components, the processors (e.g. GPUs, TPUs). This strategy is about managing power conversion through various stages efficiently and effectively to meet the demanding requirements of AI data centers. Our innovative portfolio of power semiconductors includes solutions ranging from the grid entering the data center to its core, the AI processor and leverages the benefits of Si, SiC and GaN to achieve the highest efficiency, density, and robustness.

Learn more about how we power AI

We enable AI

When intelligence moves to the edge – the role of microcontrollers

In the context of AI, a processor and a microcontroller (MCU) are both essential components, but they serve different purposes and have distinct characteristics. A processor, also known as a central processing unit (CPU), is the brain of a computer system. In the context of cloud AI, it is typically a high-performance CPU that is designed to handle complex computations and tasks in a data center.

Edge AI – where the intelligence operates at the device without the need to be connected to a cloud – opens the doors to a wide range of applications in automotive, industrial and the IoT. Edge AI is the implementation and deployment of AI applications in an edge computing environment or device close to where the data is located rather than in a central environment such as in a cloud computing facility. Common use cases are smart speakers and voice assistant technology using speech recognition analysis relying on a set of complex AI technologies. Additional trends include the smart home as well as the health, lifestyle and wearables market where AI is set to improve the efficiency of edge devices and provide frictionless user experiences.

Microcontrollers (MCUs) play a crucial role in many AI systems, including those used in edge computing, IoT devices, and other applications where low power consumption and small form factors are essential. MCUs are used in AI systems to perform a variety of tasks, such as data acquisition from sensors and other sources, which is then processed and analyzed by the AI system, data processing such as filtering, scaling, and formatting, before sending the data to a more powerful processor for further analysis, control and automation as well as communication with other devices and systems, such as sending data to the cloud or receiving commands from a central server.

Smart health appliances microchip
Smart health appliances microchip
Smart health appliances microchip

Microcontrollers (MCUs) play a crucial role in many AI systems, including those used in edge computing, IoT devices, and other applications where low power consumption and small form factors are essential. MCUs are used in AI systems to perform a variety of tasks, such as data acquisition from sensors and other sources, which is then processed and analyzed by the AI system, data processing such as filtering, scaling, and formatting, before sending the data to a more powerful processor for further analysis, control and automation as well as communication with other devices and systems, such as sending data to the cloud or receiving commands from a central server.

Learn more about how we enable and provide AI

Infineon & you - driving the AI revolution

As a global semiconductor manufacturer, we enable customers and partners to create energy-efficient, high-performance, and reliable AI applications. Our products, software, tools, and services support the development of AI-based applications, from data and ML pipelines to high-performance, low-power AI-enabled MCUs.

By understanding the role of semiconductors in AI, we can unlock the full potential of this technology and create a more efficient, connected, and intelligent world.

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