Weight data storage method and neural network processor based on method

A neural network and data storage technology, applied in the field of computer learning, can solve the problems of slow processing speed and high power consumption of neural network, achieve the effect of parallel search and reduce the number of loads

Active Publication Date: 2018-05-01
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.

Method used

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  • Weight data storage method and neural network processor based on method
  • Weight data storage method and neural network processor based on method
  • Weight data storage method and neural network processor based on method

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Embodiment Construction

[0034] In order to make the object, technical solution, design method and advantages of the present invention clearer, the present invention will be further described in detail through specific embodiments below 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.

[0035] Typically, a deep neural network has a topology with multiple layers of neural networks, and each layer of neural networks has multiple feature layers. For example, for a convolutional neural network, its data processing process consists of multi-layer structures such as convolutional layers, pooling layers, normalization layers, nonlinear layers, and fully connected layers. Among them, the operation process of the convolutional layer is: A two-dimensional weight convolution kernel of L*L size scans the input feature map. During the scanning process, the weight co...

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Abstract

The invention provides a weight data storage method and a convolution calculation method in a neural network. The weight data storage method comprises searching an effective weight in a weight convolution kernel matrix and obtaining an effective weight index, wherein the effective weight is a nonzero weight and the effective weight index is used for marking the position of the effective weight inthe weight convolution kernel matrix; and storing the effective weight and the effective weight index. By employing the weight data storage method and the convolution calculation method, the storage space can be saved and the calculation efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of computer learning, in particular to a weight data storage method 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 processing tasks b...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045G06F17/153G06F17/16G06N3/08G06F18/2163
Inventor 韩银和闵丰许浩博王颖
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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