  Deep learning is a technique for machine learning using multi-layer neural
networks. It has been used for image synthesis and image recognition, but in
recent years, it has also been used for various social detection and social
labeling. In this analysis, we compared (1) the number of Iterations per minute
between the GPU and CPU when using the VGG model and the NIN model, and (2) the
number of Iterations per minute by the number of pixels when using the VGG
model, using an image with 128 pixels. When the number of pixels was 64 or 128,
the processing time was almost the same when using the GPU, but when the number
of pixels was changed to 256, the number of iterations per minute decreased and
the processing time increased by about three times. In this case study, since
the number of pixels becomes core dumping when the number of pixels is 512 or
more, we can consider that we should consider improvement in the vector
calculation part. If we aim to achieve 8K highly saturated computer graphics
using neural networks, we will need to consider an environment that allows
computation even when the size of the image becomes even more highly saturated
and massive, and parallel computation when performing image recognition and
tuning.
