Accelerated calculation method and system of recurrent neural network and related device

Abstract

The invention provides an accelerated calculation method of a recurrent neural network. The accelerated calculation method comprises the steps of obtaining a parameter matrix multiplier of the recurrent neural network; dividing a multiplier and a multiplicand in the parameter matrix multiplier to obtain a two-dimensional matrix; inputting the two-dimensional matrix into a three-dimensional pulsation array according to a preset sequence; and determining an output result of the parameter matrix multiplication according to the three-dimensional pulsation array. According to the method and the device, the calculation time of large-size matrix multiplication is greatly shortened, and hardware resources required by calculation are reduced. The invention further provides an accelerated computingsystem of the recurrent neural network, a computer readable storage medium and a terminal, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of deep learning, and in particular to an accelerated computing method, system and related devices of a recurrent neural network. Background technique [0002] Recurrent Neural Network (RNN for short) is a type of recurrent neural network that takes sequence data as input, recurses in the evolution direction of the sequence, and connects all nodes (recurrent units) in a chain. Research on recurrent neural networks began in the 1980s and 1990s, and developed into one of the deep learning algorithms in the early 21st century, among which bidirectional recurrent neural network (Bidirectional RNN, Bi-RNN), gated recurrent unit network (Gated RecurrentUnit networks, GRU) and Long Short-Term Memory networks (LSTM) are common recurrent neural networks. [0003] In the calculation process of the recurrent neural network, its network parameters usually exist in the form of a matrix, that is, the calculation process ...

AI Technical Summary

Problems solved by technology

[0003] In the calculation process of the recurrent neural network, its network parameters usually exist in the form of a matrix, that is, the operation process involves a large number of matrix multiplication operations, but once the number of rows and columns of the matrix is ​​long, it will greatly occupy system hardware resources and is not conducive to The deep learning algorithm runs, reducing the efficiency of deep learning

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