Objective

2

In recent years

deep learning neural network has continued to develop

and excellent results have been achieved in the fields of computer vision

natural language processing

and speech recognition. In autonomous driving technology

the environment perception is an important application. The environment perception mainly processes the collected image information about the surrounding environment. Thus

deep learning is an important section in this link. However

the number of layers of existing neural network models continues to increase with the continuous increase in the complexity of processing problems. Thus

the number of overall parameters of the network and the required computing power are increasing. These models run well on platforms with sufficient computing power

such as server platforms with sufficient computing power. However

many deep neural network models are difficult to be deployed on embedded platforms with limited computing and storage resources

such as autonomous driving platforms. Compressing the existing deep neural network models is necessary to solve the contradiction between the huge amount of calculation required for the application of deep neural networks and the limited computing power of embedded platforms. This process can reduce the number of model parameters and computing power. This paper proposes a greedy network pruning method based on the existing model compression method. The propose method incorporates the probability distribution of weights to reduce redundant connections in the network model and improve the computational efficiency and parameters of the model.

Method

2

The current pruning method mainly uses the property of weight parameter as a criterion for parameter importance evaluation. The 1 norm of the convolution kernel weight parameter is used as the basis for determining the importance. However

this method ignores the variation of weight during training. In the pruning process

many methods use the trained model to perform one-time pruning. Thus

the accuracy of the model after pruning is difficult to maintain. the proposed is inspired by the study of uncertain graphs to solve the above problem. The probability distribution of weights is introduced

and the importance of the connection is jointly judged in accordance with the probability distribution of the weight parameter value and the size of the current weight in the training. The importance of the network connection and the effect of cutting the connection on the loss function are jointly used. The degree of influence collectively represents the contribution rate of this network connection to the result

thereby serving as the basis for pruning the network connection. In the stage of greedy pruning of the model

the proposed method uses incremental pruning to control the scale and speed of pruning. Iterative pruning and restoration are performed for a small proportion of connections until the state of the current sparse connections no longer changes. The pruning scale is gradually expanded until the expected model compression effect is achieved. Therefore

the incremental pruning and recovery strategy can avoid the weight gradient explosion problem caused by excessive pruning

improve the pruning efficiency and model stability

and realize dynamic pruning compared with the one-time pruning process based on the weight parameters. The proposed pruning method guarantees the maximum compression of the model's volume while maintaining its accuracy.

Result

2

The experiment uses networks of different depths for experiments

including CifarSmall

AlexNet

and visual geometry group(VGG)16

and nets with residual connections

including ResNet34 and ResNet50 networks

to verify the applicability of the proposed method to different depth networks. The experimental dataset uses the commonly used classification datasets

including CIFAR-10 and ImageNet ILSVRC(ImageNet Large Scale Visual Recognition Challenge)-2012

making it convenient for comparison with other methods. The main comparison content of the experiment includes the proposed method and the dynamic network pruning strategy on CIFAR-10. The pruning effect of the proposed method and the current state-of-the-art (SOTA) pruning algorithm HRank is compared on the Imagenet dataset in ResNet50. Experimental results prove that the accuracy of the proposed method is higher than that of the dynamic network pruning strategy at various pruning rates on the Cifar10 dataset. On the ImageNet data set

the proposed method effectively compresses the number of parameters of AlexNet and VGG16 by 5.9 and 11.4 times

respectively

with a small loss of accuracy. The number of training iterations required is more than that of the dynamic network pruning strategy. Effective compression can be performed for the residual type networks ResNet34 and ResNet50. For the ResNet50 network

a larger compression rate is achieved with a small increase in accuracy loss compared with the current SOTA method HRank.

Conclusion

2

The greedy pruning strategy fused with probability distribution solves the uncertainty problem of deep neural network pruning

improves the stability of the network after model compression

and realizes the compression of the number of network model parameters while ensuring the accuracy of the model. Experimental results prove that the proposed method has a good compression effect for many types. The probability distribution of the weight parameters introduced in this research can be used as an important basis for the subsequent parameter importance criterion in the pruning research. The incremental pruning and the connection recovery in the pruning process used in this article are important for accuracy maintenance

However

optimizing and accelerating the reasoning of the sparse model obtained after pruning needs further research.