Neural network accelerator, convolution operation implementation method and device and storage medium

Abstract

The embodiment of the invention provides a neural network accelerator, a convolution operation implementation method and device, and a storage medium. The method comprises the steps: obtaining a plurality of compressed sub-convolution kernels corresponding to an original convolution kernel and a weight index, and then reading an original data block with the specification matched with the specification of the sub-convolution kernels from to-be-processed original data; controlling the computing unit array to execute an iterative process according to the original data block, the compressed sub-convolution kernel and the weight index, wherein the computing unit array comprises a plurality of multiply-accumulate calculators; after the iterative process is finished, obtaining an output result of the computing unit array. According to the neural network convolution operation implementation scheme provided by the embodiment of the invention, the zero-value weight in the original weight convolution kernel is removed by compressing the weight in the convolution kernel, and therefore the operand of convolution operation is reduced. Meanwhile, because the array type calculation unit array is adopted to carry out convolution operation on the data and the weight, the data multiplexing degree of the convolution operation can be improved, and bandwidth occupation and power consumption in the neural network processing process are reduced.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a neural network accelerator, a convolution operation realization method, a device and a storage medium. Background technique [0002] In recent years, artificial intelligence technology has developed rapidly all over the world. The industry has invested a lot of energy in the research of artificial intelligence technology and achieved remarkable results, especially in image detection and recognition and language recognition. In terms of direction, the recognition rate of artificial intelligence has surpassed that of human beings. Neural network processing is an important processing technology for the realization of artificial intelligence. However, the continuous growth of the scale of neural networks has resulted in a typical neural network with dozens or even hundreds of layers, and hundreds of millions of connections between neurons. The performance indicators are cons...

AI Technical Summary

Problems solved by technology

Neural network processing is an important processing technology for the realization of artificial intelligence. However, the continuous growth of the scale of neural networks has resulted in a typical neural network with dozens or even hundreds of layers, and hundreds of millions of connections between neurons. The performance indicators are constantly improving, the amount of calculation is constantly increasing, and the time required to complete the reasoning is also getting longer and longer.

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