Computing method and computing device applied to neural network

Abstract

The invention provides a calculation method and a calculation device applied to a neural network. The calculation method includes the following steps: obtaining a binary convolution kernel that only includes a weight of 1 and -1; decomposing the binary convolution kernel into an initial convolution kernel and a feature convolution kernel, wherein the initial convolution The kernel and the feature convolution kernel have the same dimensions as the binary convolution kernel, the initial convolution kernel is a matrix composed of weights with a value of 1, and the feature convolution kernel is relative to the binary convolution kernel. The value convolution kernel retains a matrix formed by weights with a value of -1; performs convolution calculations in the neural network based on the initial convolution kernel and the feature convolution kernel. The calculation method and the calculation device of the present invention can improve the efficiency of convolution calculation and save the overhead of storage circuits.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a calculation method and a calculation device applied to convolutional networks. Background technique [0002] In recent years, deep learning technology has developed rapidly and has been widely used in solving high-level abstract cognitive problems, such as image recognition, speech recognition, natural language understanding, weather prediction, gene expression, content recommendation and intelligent robots. Research hotspots in academia and industry. [0003] Deep neural network is one of the perception models with the highest level of development in the field of artificial intelligence. This type of network simulates the neural connection structure of the human brain by building a model, and describes the data features layered through multiple transformation stages, providing image, video and audio Such large-scale data processing tasks have brought breakthroug...

AI Technical Summary

Problems solved by technology

[0005] At present, most deep learning applications are implemented using central processing units and graphics processing units. These technologies are not energy efficient, and there are serious energy efficiency problems and computing speed bottlenecks when applied in embedded devices or low-overhead data centers. It is difficult to meet the performance requirements of the application, and it is difficult to apply it to small and light-weight devices such as mobile phones and embedded electronic devices

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