Balanced binarization neural network quantification method and system

A binary neural and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem of model storage consumption and consumption not being well handled, and achieve improved classification performance and activation quantization loss Minimize and reduce the effect of quantization loss
CN110472725APending Publication Date: 2019-11-19BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
BEIHANG UNIV
Publication Date
2019-11-19

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Abstract

The invention discloses a balanced binarization neural network quantification method and system. The method comprises the following steps of S1, performing balance standard binarization operation on aweight in a neural network to obtain a binarized weight; S2, performing balanced binarization operation on the activation value in the neural network to obtain a binarized activation value; and S3, executing the steps S1 and S2 on the convolutional layer in the network in the iterative training process of the neural network to generate a balanced binary neural network. The balanced and standardized binarization network weight and the balanced and binarization network activation value are used, so that the neural network can achieve activation value information entropy maximization and weightand activation quantization loss minimization by minimizing a loss function in the training process, the quantization loss is reduced, and the classification performance of the binarization neural network is improved.
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Description

technical field

[0001] The invention relates to a method for quantizing a balanced binary neural network, and at the same time relates to a system for quantifying a neural network for realizing the method, which belongs to the technical field of deep learning. Background technique

[0002] Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have been well-proven in various computer vision applications, such as image classification, object detection, and visual segmentation. Traditional CNNs usually have a large number of parameters and high-performance computing requirements, and the training and inference process for a task takes a lot of time. The main reason for this problem is that the current models that achieve the best results on various tasks generally use convolutional neural networks with great depth and breadth, so that the storage model needs to use a large amount of storage resources, and the training and inference process A huge ...

Claims

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