A Pipelined Acceleration System of FPGA-Based Deep Convolutional Neural Network

Abstract

The invention brings forward a streamlined acceleration system of an FPGA-based depth convolution neural network. The streamlined acceleration system is mainly formed by an input data distribution control module, an output data distribution control module, a convolution calculating sequence serialization realizing module, a convolution calculating module, a pooling calculating sequence serialization realizing module, a pooling calculating module, and a convolution calculating result distribution control module. Moreover, the streamlined acceleration system comprises an internal system cascade interface. Through the streamlined acceleration system designed by the invention, highly efficient parallel streamlined realization can be conducted on an FPGA, problems of resource waste and effective calculation delays caused by filling operations during calculation are effectively solved, the power consumption of the system is effectively reduced, and the operation processing speed is greatly increased.

Description

technical field [0001] The invention belongs to the field of neural network calculation, and in particular relates to a pipeline acceleration system based on an FPGA-based deep convolutional neural network. Background technique [0002] With the new wave of machine learning brought about by deep learning, deep convolutional neural networks have been widely used in different large-scale machine learning problems such as speech recognition, image recognition, and natural speech processing, and have achieved a series of breakthrough research results , its powerful feature learning and classification ability has attracted widespread attention, and has important analysis and research value. [0003] The deep convolutional neural network model has the characteristics of high model depth, complex hierarchy, large data magnitude, high parallelism, intensive calculation and storage intensive, etc., and a large number of convolution operations and pooling operations often make it a pa...

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Problems solved by technology

[0003] The deep convolutional neural network model has the characteristics of high model depth, complex hierarchy, large data magnitude, high parallelism, intensive calculation and storage intensive, etc., and a large number of convolution operations and pooling operations often make it a part of the application process. Large computing bottlenecks and the storage of a large number of intermediate results also put forward higher requirements on the computer storage structure, which is very unfavorable for application scenarios with strong real-time performance and limited investment costs

[0004] The two commonly used accelerators are CPU and GPU. The CPU cannot ideally meet the requirements in terms of computing performance based on its serial execution structural characteristics. Although the GPU has obvious advantages in computing performance, it cannot break through the power consumption just like the CPU. barriers, and both CPU and GPU have serious limitations in scalability

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