Pruning method for embedded network model

Abstract

The invention discloses a pruning method for an embedded network model. The pruning method is used for solving the technical problem that an existing pruning method is poor in practicability. According to the technical scheme, the method comprises the following steps: firstly, establishing a mobienet SSD network model, and carrying out a forward operation to obtain data required by pruning calculation; Channels which are not important to a convolution layer calculation result are selected through lasso regression, and channels which have relatively low influence on a summation result in the channels are selected through a lasso algorithm; The Mobienet resolves an original layer of convolution into a channel separation convolution layer and a point convolution layer, and an input channel ofthe channel separation convolution layer is equal to an output channel of the channel separation convolution layer. According to the method, the reconstruction error is reduced to serve as the core,the lasso is used for picking out unimportant channels in all convolution layers, then channel trimming is conducted on all the convolution layers according to the special structure of the mobienet, compression acceleration of the mobienet SSD is completed, and the practicability is good.

Description

technical field [0001] The invention relates to a pruning method, in particular to an embedded network model-oriented pruning method. Background technique [0002] With the development of deep convolutional network models in the field of artificial intelligence, the performance of large-scale network models is getting higher and higher. However, when deployed on some embedded devices, they are limited to limited computing resources and run too slowly. [0003] For the accelerated compression method of the neural network model, the key is to use an algorithm to pick out those unimportant nodes in the network, and then delete these redundant nodes to complete the compression acceleration of the network model. In the literature "Designing energyefficient convolutional neural networks using energy-awarepruning.arXiv preprint arXiv:1611.05128, 2016.", Yang et al. performed pruning based on the method of parameter values, cutting out those nodes with relatively low parameter value...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing pruning methods, the present invention provides a pruning method for embedded network models

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