Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

In today’s rapidly evolving technology landscape,computing power has become an important driving force for social progress and industrial upgrades.
In this vast ocean of computing,CPU, GPU, ASIC, and FPGA are the four core forces, each playing an irreplaceable role.
Let’s dive into the secrets of these four computing units.

Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

1. CPU
CPU (Central Processing Unit), which you are probably familiar with, is the core of computation and control in computers, serving as the final execution unit for information processing and program execution.
Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

The CPU is a processor under the von Neumann architecture, where instructions and data must be accessed from the same storage space and transmitted via the same bus, which prevents overlapping execution. This process determines that the CPUis good at decision-making and control, but is less efficient in multi-data processing tasks.

Generally, the improvement of CPU computing power relies mainly on two aspects, namelyclock frequency and core count. The operations of a computer are executed step by step under the control of clock signals, completing one operation per clock cycle. The level of clock frequency largely reflects the speed of the CPU. The CPU core is a single processing unit within the CPU that can execute instructions.

Typically, the higher the clock frequency and the more cores, the stronger the CPU performance, but this also brings aboutexcessive power consumption and overheating, which, if the cooling cannot keep up, may lead to CPU damage.

Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

As CPU computing power gradually reaches a bottleneck, it increasingly fails to meet the exponentially growing demand for computing power. The direction of computing power development is increasingly shifting toward specialization, seeking higher performance, lower power consumption, and cost.

02. GPU

GPU (Graphics Processing Unit), as the name suggests, is primarily responsible for performing image and graphics-related computations.

Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

You may wonder why we need a dedicated GPU to handle graphic tasks, and why can’t the CPU do it?

Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

This is because the GPU operates on aparallel programming model, which is completely different from the CPU’s serial programming model. Due to the high parallelism of graphic rendering tasks, the GPU can effectively improve processing power and memory bandwidth simply by increasing parallel processing units and memory control units.
The relationship between GPU and CPU is like that of many elementary school students and a university professor; although the university professor has more profound knowledge and can handle more complex computational problems, when it comes to processing many simple calculations, the speed of a university professor is not as fast as that of a group of elementary school students.
Of course, with the development of technology, the application range of GPUs has expanded to scientific computing, artificial intelligence, machine learning, and other fields.
03. ASIC
The aforementioned CPU and GPU can meet the needs of general scenarios, but as computing scenarios gradually become more segmented, general-purpose computing chips can no longer meet user needs, leading to the gradual application of ASIC chips.
ASIC (Application Specific Integrated Circuit) is an integrated circuit designed for specific applications.
Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide
ASIC’s design is completely optimized forspecific applications, using hardwired methods to implement circuit functions, achieving higher efficiency and lower power consumption when handling specific tasks, thus reaching the extreme in performance and efficiency.
It’s like custom tailoring in the fashion industry; tailored clothes often better meet customer needs. Although wearing a T-shirt and shorts to a party is possible, it is not appropriate. Choosing an outfit that matches the occasion undoubtedly boosts confidence and allows one to better integrate and enjoy that special night.
Of course, when it comes to custom tailoring, the first thought is often “expensive.” The high customizability of ASIC also means high R&D costs and technical barriers. Because ASIC chips are designed for specific applications, they require specialized circuit structure and layout design, which usually requires highly specialized technology and rich experience. The process of custom design is complex and time-consuming, increasing R&D costs and technical barriers. Moreover, ASICs have low flexibility; once designed, they are difficult to change, making it hard to capture more market share in this rapidly evolving technology era.
Therefore, ASICs are usually suitable for applications that have extremely high performance requirements and relatively stable demands, such as cryptocurrency mining and high-performance computing.
04. FPGA
Once designed, ASIC chips cannot be changed. So what should users do when they have other needs?
This brings us toFPGA (Field Programmable Gate Array), which, as the name suggests, is a type of programmable integrated circuit that can be configured by users to perform specific tasks.
Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide
Compared to the von Neumann architecture of CPUs and GPUs, FPGAs adopt a design without instructions and shared memory, where the function of each logic unit is determined during reprogramming, making FPGAs more energy-efficient than CPUs and GPUs.
As for performance compared to ASICs, as mentioned earlier, ASIC chips are custom-made, thus offering stronger performance and lower energy consumption, but due to higher technical barriers and longer design cycles, they are also more expensive. However, when ASIC chips are needed on a large scale, costs can significantly decrease.
FPGA can bereconfigured, thus significantly enhancing flexibility. This is similar to building blocks; fixed blocks require design → mold opening → injection molding production → decoration and coloring → packaging before they can be sold. Meanwhile, intelligent blocks only need to produce a few different shapes and colors of blocks, allowing consumers to build according to their imagination and creativity. The downside is that the process of building blocks can create redundancy, leading to waste.
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Conclusion
Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide
CPU, GPU, FPGA, and ASIC are the four cornerstones of the computing world, each playing an important role in different application scenarios.They each have their own strengths, collectively driving technological progress and development.
In the future, as technology continues to advance and application needs continue to change, these four computing units will continue to evolve and integrate, bringing us a more efficient, flexible, and intelligent computing experience. Let us look forward to the arrival of this era of computing full of infinite possibilities!

Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

Source: ZTE Documentation

Original Title: A Comprehensive Guide to Understanding CPU, GPU, ASIC, and FPGA

Editor: Decoherence

Reprinted content only represents the author’s views

Does not represent the position of the Institute of Physics, Chinese Academy of Sciences

If reprinting is required, please contact the original public account

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Understanding CPU, GPU, ASIC, and FPGA: A Comprehensive Guide

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