Technologies for Practical Application of Deep Learning

Deep learning, a machine learning method, is attracting more and more attention. Research and development of deep learning have accelerated as it achieves recognition accuracy that far surpasses that of conventional methods of extracting features manually. Two issues are affecting its practical application: lengthy training and limited graphical processing unit (GPU) memory. As neural networks are being enlarged to enable higher recognition accuracy, these two issues are becoming more and more serious. In this paper, we introduce three technologies targeting them: distributed parallel processing for faster training, memory optimization for increased GPU memory, and a dedicated hardware engine architecture for data size reduction.