.png){kind=icon}
返回
GPU accelerated training of image convolution filter weights using genetic algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.02.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Genetic algorithms (GA) provide an efficient method for training filters to find proper weights using a fitness function where the input signal is filtered and compared with the desired output. In the case of image processing applications, the high computational cost of the fitness function that is evaluated repeatedly can cause training time to be relatively long. In this study, a new algorithm, called sub-image blocks based on graphical processing units (GPU), is developed to accelerate the training of mask weights using GA. The method is developed by discussing other alternative design considerations, including direct method (DM), population-based method (PBM), block-based method (BBM), and sub-images-based method (SBM). A comparative performance evaluation of the introduced methods is presented using sequential and other GPUs. Among the discussed designs, SBM provides the best performance by taking advantage of the block shared and thread local memories in GPU. According to execution duration and comparative acceleration graphs, SBM provides approximately 55-90 times more acceleration using GeForce GTX 660 over sequential implementation on a 3.5 GHz processor. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
GPU computing
CUDA
Genetic algorithms
Image processing
Convolution filter
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Genetic algorithm approach for designing 2-D FIR digital filters with 2-D symmetric properties.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
Distribution of GABAA and GABAB receptors in mammalian brain: Potential targets for drug development
OAAI
OAAI分享收藏分享收藏Design of two-dimensional FIR digital filters for sampling structure conversion by genetic algorithm approachSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Comparison sorting on hybrid multicore architectures for fixed and variable length keys固定和可变长度密钥的混合多核体系结构的比较排序
PREAI
分享收藏
查看更多内容