Exploring Deep Learning Models for Compression and Acceleration - DZone AI

As the scale of deep learning networks increases, the computational complexity also increases accordingly, which severely limits its application to smart devices such as mobile phones. For example, the use of large-scale complex network models such as VGGNet and the residual network on an end device is not realistic. Therefore, we need a deep learning model to perform compression and acceleration. We have described two major compression algorithms below. The Low Bit model refers to compressing consecutive weights into discrete low-precision weights.