All You Need to Know About AI Chips

    All You Need to Know About AI Chips

    The Future of Artificial Intelligence (AI) greatly hinges on AI Chips. This article explores the AI Chips landscape and its real world applications.

    According to Statista's AI chip market report - In 2022, the artificial intelligence (AI) chip market reached USD 44.2 billion. This figure is forecast to increase to USD 120 billion in 2027 as the hype around AI applications grows.

    How did AI Chips Evolve?

    As the demand for AI apps grows, chip manufacturers are looking for ways to create more powerful and efficient AI chips. Traditionally, Graphics Processing Units (GPUs) were used for this purpose. Still, they have now been overtaken by newer techs such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs).

    • Graphics Processing Units (GPUs)

    GPUs were designed to make video games look really good. But they turned out to be good at helping computers learn, too.

    GPUs are better than average computer chips at doing many small tasks simultaneously, which is important for many AI tasks.

    However, there are now even better chips designed specifically for AI tasks. They can sometimes be more efficient than GPUs.

    • ASICs and FPGAs

    AI requires specialized hardware to perform difficult computations like Machine Learning (ML) and neural network processing. Two types of chips commonly used in AI apps are ASICs and FPGAs.

    ASICs are Application-Specific Integrated Circuits designed specifically for a particular task or app. In the case of AI, ASICs are designed to handle specific AI tasks, such as neural network processing.

    Because they are built for a specific task, they are highly efficient at that task and can perform it faster than other types of chips. However, they are less flexible than other chips. They cannot be easily reprogrammed to perform other tasks.

    On the other hand, FPGAs are Field-Programmable Gate Arrays. They can be programmed to perform a wide range of tasks. They are more flexible than ASICs. This makes them a great choice for various AI workloads.

    However, they are generally more complex and expensive than other chips, and programming them can be more complex.

    Overall, ASICs and FPGAs define the evolution of AI chip tech, offering specialized hardware that can handle the complex computations required for AI apps. The type of chip to use depends on the app’s specific requirements.

    • Neural Processing Units (NPUs)

    Neural Processing Units (NPUs) are chips designed to process neural networks, an important component of modern AI systems. Neural networks require high-volume, parallel computations. This includes matrix multiplication and activation function computation, for which NPUs are optimized.

    They typically have many small, efficient processing cores that can perform simultaneous operations. These cores are designed to handle typical mathematical tasks commonly used in neural networks, such as floating-point operations and tensor processing. In addition, they have high-bandwidth memory interfaces. This efficiently manages the large amount of data that neural networks require.

    One crucial aspect of its design is power efficiency. Neural network computations can be power-intensive. NPUs usually incorporate features that optimize power consumption.

    For example, they can have dynamic power scaling based on computational demand. This means they can adjust their power consumption based on the workload. Also, they can have circuit designs that reduce energy usage per operation.

    Central Processing Units (CPUs) vs. AI Chips

    While AI chips may have a higher initial cost than CPUs, they are designed with specific needs that cater to various ML algorithms. Therefore, they prove to be a valuable investment in the long run.

    On the other hand, CPUs seem more affordable and popular due to their versatility in performing general task computations and calculations.

    Also read: CUDA vs. OpenCL: A Comparative Analysis of GPU Programming Frameworks

    Real-World Applications of AI Chips

    AI tech is advancing rapidly, and AI chips are expected to be integrated into more devices and systems shortly.

    This will lead to a world where people interact with AI regularly.

    • Data Centers Using AI Chips

    AI chips are used in data centers to provide faster and more efficient processing of AI workloads while reducing energy consumption.

    This benefits the environment by reducing energy usage and enables data centers to process large amounts of data more effectively. This leads to better results for firms that rely on AI.

    • AI Powered Mobile Phones

    Mobile phone manufacturers like Samsung and Apple use AI chips to improve their devices’ processing speed, battery life, and capabilities. This tech is expected to become more commonly used in upcoming devices.

    Integrating AI chips can improve features like facial recognition and energy optimization. This brings greater convenience and privacy for users.

    The recent launch of the Samsung S24 series powered by AI has revealed the possibilities AI can offer.

    • Edge Devices and AI Processing

    More and more devices with built-in AI algorithms are being used at the network’s edge. This allows for faster processing, lower costs, and better efficiency for AI apps. Edge-based operations are pushing the boundaries of what AI can do, meeting the increasingly high demands of modern usage.


    AI chips are revolutionizing the world of AI and its real-world applications. They offer hardware that can handle complex computations required for AI apps. The type of chip to use depends on the app’s specific needs.

    The evolution of AI chip tech has led to the development of ASICs, FPGAs, NPUs, and other chips that can handle various AI workloads. These chips are being integrated into more devices and systems, leading to a world where people interact with AI regularly.

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