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Weight storage method in nerve network and processor based on same

A neural network and processor technology, applied in biological neural network models, neural architecture, physical implementation, etc., can solve the problems of slow neural network processing speed and high operating power consumption, and achieve the effect of reducing loading and storage space.

Active Publication Date: 2018-09-07
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0004] However, in the prior art, the neural network has problems such as slow processing speed and high power consumption.

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  • Weight storage method in nerve network and processor based on same
  • Weight storage method in nerve network and processor based on same
  • Weight storage method in nerve network and processor based on same

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Embodiment Construction

[0039] 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.

[0040] Typically, a deep neural network has a multi-layer topology. For example, a convolutional neural network consists of several convolutional layers, pooling layers, and fully connected layers. The operation process of a convolutional layer is: a L*L The weight convolution kernel of large and small sizes scans the input feature map. During the scanning process, the weight convolution kernel and the neurons in the corresponding convolution domain in the feature map calculate the inner product, and sum the inner product values ​​of all convolution domains to obtain the convo...

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Abstract

The invention provides a weight storage method in a nerve network and a nerve network memory based on same; the weight storage method comprises the following steps: building an original two dimensionweight convolution kernel into a three dimensional space matrix; searching valid weights in the three dimensional space matrix and building valid weight indexes, wherein the valid weights are non-zeroweights, and the valid weight indexes are used for marking positions of the valid weights in the three dimensional space matrix; storing the valid weights and the valid weight indexes. The weight data storage method and a convolution calculating method can save the storage space and can improve the computing efficiency.

Description

technical field [0001] The invention relates to the technical field of computer learning, in particular to a weight storage method in a neural network and a neural network processor based on the method. Background technique [0002] In recent years, deep learning technology has developed rapidly and has been widely used in solving advanced abstract cognitive problems, such as image recognition, speech recognition, natural language understanding, weather prediction, gene expression, content recommendation and intelligent robots, and has become an academic Research hotspots in the world and industry. [0003] Deep neural network is one of the perception models with the highest level of development in the field of artificial intelligence. It simulates the neural connection structure of the human brain by establishing a model, and describes the data characteristics hierarchically through multiple transformation stages, providing images, videos, audios, etc. Large-scale data pro...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045
Inventor 韩银和闵丰许浩博王颖
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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