Hardware acceleration implementation system and method for RNN forward propagation model based on transverse pulsation array

Abstract

The invention discloses a hardware acceleration implementation system and a method for an RNN forward propagation model based on a transverse pulsation array. The method comprises the steps of firstly, configuring network parameters, initializing data, lateral systolic array, wherein a blocking design is adopted in the weight in calculation; partitioning a weight matrix calculated by the hidden layer according to rows; carrying out matrix multiplication vector and vector summation operation and activation function operation; calculating hidden layer neurons, obtaining hidden layer neurons according to the obtained hidden layer neurons; performing matrix multiplication vector, vector summation operation and activation function operation; generating an RNN output layer result; finally, generating an output result required by the RNN network according to time sequence length configuration information; according to the method, a hidden layer and an output layer are parallel in a multi-dimensional mode, the pipelining performance of calculation is improved, meanwhile, the characteristic of weight matrix parameter sharing in the RNN is achieved, the partitioning design is adopted, the parallelism degree of calculation is further improved, the flexibility, expandability, the storage resource utilization rate and the acceleration ratio are high, and calculation is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence hardware acceleration, and in particular relates to a system and method for realizing hardware acceleration of an RNN forward propagation model based on a transverse pulsation array. Background technique [0002] In the era of artificial intelligence, neural network algorithms have been widely used in different fields such as image recognition, such as convolutional neural networks (Convolutional Neural Networks, CNN), deep neural networks (Deep Neural Networks, DNN), in such a wide and frequent use , the neural network algorithm also exposed some problems: the traditional neural network can only process one input alone, and the input at the previous and subsequent moments is completely irrelevant. For example, when we predict the next word of a sentence, the preceding and following words in the sentence are not independent, and CNN or DNN can't do anything about it, while Recurren...

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Problems solved by technology

[0002] In the era of artificial intelligence, neural network algorithms have been widely used in different fields such as image recognition, such as convolutional neural networks (Convolutional Neural Networks, CNN), deep neural networks (Deep Neural Networks, DNN), in such a wide and frequent use , the neural network algorithm also exposed some problems: the traditional neural network can only process one input alone, and the input at the previous and subsequent moments is completely irrelevant

[0003] The input layer, hidden layer, and output layer of RNN are all fully-connected computing modes, including a large number of multiply-accumulate operations, which are computationally intensive algorithms that usually need to be implemented using hardware acceleration methods, but traditional CPUs have fewer logical operation units. It cannot meet the increasingly large computing network. Although GPU has strong computing power, it cannot flexibly configure the hardware structure. As a dedicated processing chip, ASIC has low power consumption, small area, high performance and poor flexibility and cannot be configured for specific needs.

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