One-dimensional convolution acceleration device and method for complex neural network

Abstract

The invention provides a one-dimensional convolution acceleration device of a complex neural network. The one-dimensional convolution acceleration device comprises an acceleration device module and anexternal storage module, the acceleration device module comprises a plurality of calculation units and a buffer calculation unit; the buffer calculation unit comprises a one-dimensional convolution calculation unit, a weight buffer and a feature map buffer; the plurality of calculation units are used for respectively calculating a real part and an imaginary part and outputting results to the output feature map storage area; and the one-dimensional convolution calculation unit is used for reading data from the weight buffer and the feature map buffer and performing calculation respectively, and a calculation result is output to the plurality of calculation units. The invention further provides a one-dimensional convolution acceleration method of the complex neural network based on the device. According to the method, the utilization rate of a computing unit can be improved, parallel real part and imaginary part computing is carried out for a complex number value storage format, the problem of channel intersection of complex convolution is solved, and one-dimensional convolution computing of the complex neural network is accelerated.

Description

technical field [0001] The invention belongs to the field of hardware acceleration realization design of a neural network algorithm, in particular to a one-dimensional convolution acceleration device and method of a complex neural network. Background technique [0002] A complex neural network refers to a neural network structure in which weight parameters and feature maps are represented by complex numbers, including network layers such as complex convolutions, complex activation functions, and complex batch normalization. Complex neural networks are mainly used in fields that require two parameters, amplitude and phase, to describe data. For example, in the radio frequency field, radio fingerprint identification transmission is carried out based on I / Q signals, and in the audio field, automatic music transcription, speech recognition, etc. The input data for this type of problem is a one-dimensional complex time series, and the processing involves the calculation of compl...

AI Technical Summary

Problems solved by technology

However, these works are designed for the calculation characteristics of real neural networks, and only support the calculation form of the convolution kernel and the corresponding channel of the input feature map, and do not support the convolution calculation of cross channels.

If these studies are directly applied to the acceleration of complex neural networks, the computational performance will be reduced due to the storage format of complex values ​​and the cross-convolution characteristics of complex convolution.

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