Method and system for accelerating deep learning algorithm on field programmable gate array platform

Abstract

The invention discloses a method and system for deep learning algorithm acceleration on a field-programmable gate array platform. The field-programmable gate array platform is composed of a universal processor, a field-programmable gate array and a storage module. The method comprises: according to a deep learning prediction process and a training process, a general computation part that can be operated on a field-programmable gate array platform is determined by combining a deep neural network and a convolutional neural network; a software and hardware cooperative computing way is determined based on the determined general computation part; and according to computing logic resources and the bandwidth situation of the FPGA, the number and type of IP core solidification are determined, and acceleration is carried out on the field-programmable gate array platform by using a hardware computing unit. Therefore, a hardware processing unit for deep learning algorithm acceleration is designed rapidly based on hardware resources; and compared with the general processor, the processing unit has characteristics of excellent performance and low power consumption.

Description

technical field [0001] The invention relates to the field of computer hardware acceleration, in particular to a method and system for accelerating deep learning algorithms on a field programmable gate array platform. Background technique [0002] Deep learning has achieved remarkable results in solving high-level abstract cognitive problems, bringing machine learning to a new level. It not only has high scientific research value, but also has strong practicality, which makes it very popular in both academia and industry. However, in order to solve more abstract and complex learning problems, the network size of deep learning is increasing, and the complexity of calculation and data is also increasing dramatically. For example, the Google Cat system network has about 1 billion neurons. Accelerating deep learning-related algorithms with high performance and low energy consumption has become a research hotspot for scientific research and commercial institutions. [0003] Usua...

AI Technical Summary

Problems solved by technology

However, the power consumption of the GPU is relatively high, and the power consumption of a single GPU is often higher than that of the mainstream CPU in the same period, usually dozens of times or even hundreds of times more energy consumption than FPGAs.

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