GPU-based parallel optimization of immune convolutional neural network and embedded system
| Autor: | Zixing Cai, Jizheng Guo, Tao Gong, Tiantian Fan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Time delay neural network 020209 energy Deep learning Computer Science::Neural and Evolutionary Computation Real-time computing Basis function 02 engineering and technology Convolutional neural network Probabilistic neural network Immune system Computer engineering Artificial Intelligence Control and Systems Engineering Factor (programming language) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business computer Smoothing computer.programming_language |
| Zdroj: | Engineering Applications of Artificial Intelligence. 62:384-395 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2016.08.019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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