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.
翻译：深度学习是使用多层神经网络进行机器学习的一种技术。 它一直用于图像合成和图像识别, 但近年来, 它也被用于各种社会检测和社会标签。 在这一分析中, 我们比较:(1) 使用 VGG 模型和 NIN 模型时, GPU 和 CPU 之间的每分钟迭接次数, 以及(2) 使用 VGG 模型时, 每分钟迭接次数, 使用 128 像素的像素数。 当像素数为64 或 128 时, 当使用 GPU 时, 处理时间几乎相同, 但当像素数改变为 256 时, 处理时间也几乎相同。 在使用 GPU 时, 我们比较了 GPU 和 CPU 每分钟的迭接次数, 处理时间大约增加了三次 。 在使用 VGGGM 模型和 NIN 模型时, 当像素数为 512 或 以上时, 每分钟 的迭接连像素数, 我们就可以认为我们应该考虑改善矢量计算部分。 如果我们的目标是使用 神经网络 达到 8K 高度饱和 的计算机图形图形图形图形图形, 我们就需要考虑一个环境, 甚至当进行高度的计算时, 当图像的计算时, 能够进行高度的同步的计算时, 甚至在进行高度的同步的计算时, 和高度调整时, 。
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