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Calculation method and device applied to neural networks

A neural network and computing method technology, applied in the field of deep learning, can solve the problems of difficulty in applying miniaturized and lightweight devices, difficult to meet performance requirements, and low technical energy efficiency, so as to reduce on-chip data transmission bandwidth, reduce overhead, and improve The effect of computational efficiency

Active Publication Date: 2018-04-20
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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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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  • Calculation method and device applied to neural networks
  • Calculation method and device applied to neural networks
  • Calculation method and device applied to neural networks

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Embodiment Construction

[0042] In order to make the purpose, technical solution, design method and advantages of the present invention clearer, the present invention will be further described in detail through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0043] Typically, a neural network structure includes an input layer, multiple hidden layers, and an output layer, figure 1 A schematic diagram of the neural network model is shown, assuming Represents several nodes of a certain layer in the neural network, which are connected to the node y of the next layer. Indicates the weight of the corresponding connection, and the value of y is calculated by the function f. For example, for a convolutional neural network, its data processing process consists of multi-layer structures such as convolutional layers, pooling layers, nor...

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Abstract

The invention provides a calculation method and device applied to neural networks. The calculation method comprises the following steps of: obtaining a binary convolutional kernel which only comprisesa numerical value 1 and a -1 weight; decomposing the binary convolution kernel into an initial convolution kernel and a feature convolutional kernel, wherein the initial convolution kernel and the feature convolution kernel are same as the binary convolution kernel in the aspect of dimensionality, the initial convolution kernel is a matrix formed by a weight with a numerical value of 1, and the feature convolution kernel is a matrix formed by a weight with a numerical value of -1 relative to the binary convolution kernel; and carrying out convolution calculation in a neural network on the basis of the initial convolution kernel and the feature convolution kernel. By utilizing the calculation method and device provided by the invention, the convolution calculation efficiency can be improved and the storage circuit overhead can be saved.

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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04
CPCG06N3/045
Inventor 韩银和许浩博王颖
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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