FPGA device for realizing on-chip network transmission bandwidth expansion function

Abstract

The invention discloses an FPGA device for realizing an on-chip network transmission bandwidth expansion function, relating to the technical field of FPGA. When a predetermined function module with abuilt-in hard core IP node is integrated in a FPGA bare chip, a soft-core IP node and a hard-core IP node are formed by utilizing logic resource module configuration in the FPGA bare chip and are connected to form an NOC network structure, so that the addition of nodes is achieved, and transmission bandwidth of the predetermined function module can be expanded; the soft core IP node can be additionally connected to the input and output signals in the predetermined function module, and the transmission bandwidth of the predetermined function module can be expanded. Furthermore, the FPGA devicealso comprises a silicon connection layer in which a silicon connection layer NOC network is arranged, so that nodes in the FPGA bare chip can be connected to the silicon connection layer to form a larger NOC structure, the increase of the nodes is further realized, and the transmission bandwidth of a predetermined function module is expanded.

Description

technical field [0001] The invention relates to the field of FPGA technology, in particular to an FPGA device for realizing the function of expanding network transmission bandwidth on a chip. Background technique [0002] With the continuous improvement of integrated circuit manufacturing technology and the sharp increase in the amount of data, artificial intelligence has achieved rapid development in recent years, especially in March 2016 AlphaGo and the world champion of Go, professional nine-dan Go player Li Shishi played Go man-machine battle, Winning with a total score of 4 to 1 pushed artificial intelligence to unprecedented heights. The rapid development of human intelligence is inseparable from the improvement of the efficiency of big data, high-performance computing and machine learning algorithms. In the stage where machine learning algorithms are still being iteratively optimized, FPGAs have unique advantages in the field of artificial intelligence due to their p...

AI Technical Summary

Problems solved by technology

In the stage where machine learning algorithms are still being iteratively optimized, FPGAs have unique advantages in the field of artificial intelligence due to their programmability and powerful vector computing capabilities. The FPGA field is also optimizing the FPGA architecture and adding dedicated Artificial intelligence engine, so now there are some ways to design and integrate a dedicated AI module inside the FPGA die, but after the AI ​​module is integrated inside the FPGA die, it is limited by the chip structure, and the transmission bandwidth and computing efficiency are relatively limited. It is difficult to achieve the expected work effect

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