Pipeline-based neural network processing system and processing method

A neural network and processing system technology, applied in biological neural network models, neural architectures, etc., can solve problems such as high operating power consumption and slow neural network processing speed, and achieve the goal of increasing speed, improving resource utilization, and improving computing efficiency Effect

Active Publication Date: 2018-03-30
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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[0004] However, in the prior art, the neural network has prob

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  • Pipeline-based neural network processing system and processing method
  • Pipeline-based neural network processing system and processing method
  • Pipeline-based neural network processing system and processing method

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[0036] 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.

[0037] Typically, a deep neural network is a topology with multiple layers of neural networks, and each layer of neural network 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: Scan the input feature map with a two-dimensional weight convolution kernel of K*K size. During the scanning process, the weight and t...

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Abstract

The present invention provides a neural network processing system. The processing system comprises a multiplier module, wherein the multiplier module contains a multi-level structure constituting a pipeline, and is configured to perform a multiplication operation of to-be-calculated neurons and weights in a neural network, and each level structure of the multiplier module completes the sub-operation of the multiplication of the neuron and the weight; an accumulator module, wherein the accumulator module comprises a multi-level structure constituting the pipeline, and is configured to perform an accumulation operation on the multiplication result of the multiplier module to obtain the output neurons of the convolutional layer in the neural network, and each level structure of the accumulator module completes the sub-operations of the accumulation operation; a pooling unit, wherein the pooling unit is configured to perform the pooling processing on the output neurons of the convolutionallayer; and a control unit, wherein the control unit is configured to control the transfer of data in the neural network processing system. By using the neural network processing system of the presentinvention, resource utilization and the data processing speed can be improved.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a pipeline-based neural network processing system and processing method. 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. 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 bring breakthrough progress. A deep ne...

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Application Information

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