GPU accelerated training of image convolution filter weights using genetic algorithms
This paper proposes a fast algorithm for training image filter using GPU. Parallelization of genetic algorithms is realized by masterslave method. Sub-image based (SBM) method is proposed to use the GPU efficiently. SBM is developed by discussing other alternative design considerations. Experimental results show about 50 to 90 acceleration using GeForce GTX 660.
convolution filter genetic algorithms gpu computing cuda image processing