System for circular convolution calculation data reuse of convolutional neural network

Abstract

The invention discloses a coarse-grained reconfigurable system-oriented convolution neural network loop convolution calculation data reuse system, including a main controller and a connection control module, an input data reuse module, a convolution loop operation processing array, and a data transmission Pathways in four parts. During the convolution cycle operation, the essence is to multiply multiple two-dimensional input data matrices with multiple two-dimensional weight matrices. Generally, these matrices are large in size, and the multiplication takes up most of the time of the entire convolution calculation. The present invention utilizes a coarse-grained reconfigurable array system to complete the convolution calculation process. After receiving the convolution operation request instruction, the register rotation method is used to fully explore the reusability of the input data in the convolution cycle calculation process, which improves the data utilization rate. It also reduces the bandwidth access pressure, and the designed array unit is configurable, and can complete convolution operations with different cyclic convolution scales and step sizes.

Description

technical field [0001] The invention relates to the field of embedded reconfigurable design, in particular to a system for reusing convolutional neural network cyclic convolution calculation data for coarse-grained reconfigurable systems, which can be used in high-performance reconfigurable systems to realize convolutional neural networks Carry out a large number of circular convolution operations, use existing data as much as possible, reuse data, improve operation speed, and reduce data reading bandwidth pressure. Background technique [0002] Reconfigurable processor architecture is an ideal platform for application acceleration. Because the hardware structure can be reorganized according to the data flow graph of the program, reconfigurable arrays have been proved to have good performance improvement potential for scientific computing or multimedia applications. [0003] Convolution operation has a wide range of uses in the field of image processing. For example, convolu...

AI Technical Summary

Problems solved by technology

The data reuse rate is very high, but the traditional way of caching or directly reading from the outside, due to the limitation of data reading bandwidth and the lack of configurable arrays, the efficiency of completing multi-layer convolution loop operations is low

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