What exactly is FPGA? Can it really replace the CPU architecture?

Have you heard of FPGA yet? Then you must have not updated your knowledge in the enterprise IT field for a long time. Today I will talk to you about what is FPGA? What are the main application scenarios of FPGAs? Some people say that FPGA is the future to replace traditional CPU and GPU, do you believe it?

FPGA, the full name of Field-Programmable Gate Array (Field-Programmable Gate Array), originally appeared as a semi-custom circuit in the field of application specific integrated circuits. It has a certain degree of programmability and can perform data parallel and task parallel computing at the same time. There is more obvious efficiency when dealing with specific applications.

Inspur HPC "accelerated"

In fact, Intel, Tsinghua Unigroup, Inspur and other companies have all begun to deploy FPGAs. As early as the SC2015 conference, Inspur teamed up with Altera and iFlytek, China’s largest intelligent voice technology provider, to jointly release a set of deep learning-oriented platforms based on Altera Arria 10 FPGA.

Intel releases Xeon E5 2600 v4 processor

Of course, the most famous is Intel’s $16.7 billion acquisition of Altera, which is also the largest acquisition in Intel’s history. The main reason Intel acquired Altera is FPGA. According to the Intel Xeon E5 2600 v4 processor integrated with FPGA chips that Intel exhibited at IDF, the $16.7 billion acquisition is worth the money: Xeon processors have improved performance per watt with the help of FPGA chips. 70%.

Tsinghua Unigroup is another company that hopes to have direct access to the latest FPGA technology through acquisitions. Following the failure of the acquisition of Micron and the acquisition of the hard drive manufacturer WD, Tsinghua Unigroup may acquire shares of Lattice Semiconductor in the United States. Get into the FPGA market and make a layout.

So, what is the reason why FPGA attracts so many manufacturers crazy?

From the perspective of application scenarios, we can see that after Google’s Alpha Dog defeated the human Go champion, deep learning has gone from the altar, and more and more people are beginning to realize that deep learning may change their lives in the future. It has become the direction of future technology development; FPGA design tools make it more compatible with upper-layer software that is often used in the field of deep learning. FPGA is a major technology that helps deep learning.

However, if FPGA is the future that succeeds traditional CPU and GPU, it is a bit exaggerated. Regardless of whether the CPU and GPU technologies are mature and have a complete ecological chain, the structure of the CPU and FPGA is also different. The CPU has processes such as control instruction fetching, decoding, etc., capable of processing all kinds of strange instructions and requirements;

CPU architecture

In contrast, FPGAs cannot handle all kinds of unseen instructions as flexibly as CPUs, and can only process input data and output according to a fixed pattern. This is why FPGAs are often regarded as an expert-specific architecture. .

FPGA architecture

Different from CPU, FPGA and GPU have a large number of computing units, so their computing power is very strong. When performing neural network operations, the speed of both will be much faster than the CPU. However, the instructions natively supported by the GPU due to the fixed architecture and hardware are fixed, while the FPGA is programmable.

GaxPy algorithm performance comparison (unit: microsecond)

We can see that the application areas of FPGA are mainly deep learning and neural network algorithms, while traditional CPUs pay more attention to "general purpose". Although GPUs pay more attention to computing speed, their instructions are still fixed. The emergence of FPGA is so popular all over the world because of its programmability, which gives FPGA a unique advantage in the field of deep learning. It is not surprising that Google developed its own chip called TPU in order to develop deep learning. As Holzer, head of Google's data center, said: Google's research and development of its own chips is to solve the problems that the provinces solve.

The author believes that when market demand changes, technology will surely develop with it. When deep learning becomes a hot field, the FPGA that best matches it will also become the focus of manufacturers' pursuit.

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