GPU-based parallel optimization of immune convolutional neural network and embedded system

Abstract

Up to now, the image recognition system has been utilized more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. In designing the image recognition system, the traditional convolutional neural network has some defects such as long training time, easy over-fitting and high misclassification rate. In order to overcome these defects, we firstly used the immune mechanism to improve the convolutional neural network and put forward a novel immune convolutional neural network algorithm, after we analyzed the network structure and parameters of the convolutional neural network. Our algorithm not only integrated the location data of the network nodes and the adjustable parameters, but also dynamically adjusted the smoothing factor of the basis function. In addition, we utilized the NVIDIA GPU (Graphics Processing Unit) to accelerate the new immune convolutional neural network (ICNN) in parallel computing and built a real-time embedded image recognition system for this ICNN. The immune convolutional neural network algorithm was improved with CUDA programming and was tested with the sample data in the GPU-based environment. The GPU-based implementation of the novel immune convolutional neural network algorithm was made with the cuDNN, which was designed by NVIDIA for GPU-based accelerating of DNNs in machine learning. Experimental results show that our new immune convolutional neural network has higher recognition rate, more stable performance and faster computing speed than the traditional convolutional neural network.