Phenomenally successful in practical inference problems, convolutional neural
networks (CNN) are widely deployed in mobile devices, data centers, and even
supercomputers. The number of parameters needed in CNNs, however, are often
large and undesirable. Consequently, various methods have been developed to
prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited
benefits. While pruning the fully connected layers reduces a CNN's size
considerably, it does not improve inference speed noticeably as the compute
heavy parts lie in convolutions. Pruning CNNs in a way that increase inference
speed often imposes specific sparsity structures, thus limiting the achievable
sparsity levels.
We present a method to realize simultaneously size economy and speed
improvement while pruning CNNs. Paramount to our success is an efficient
general sparse-with-dense matrix multiplication implementation that is
applicable to convolution of feature maps with kernels of arbitrary sparsity
patterns. Complementing this, we developed a performance model that predicts
sweet spots of sparsity levels for different layers and on different computer
architectures. Together, these two allow us to demonstrate 3.1--7.3$\times$
convolution speedups over dense convolution in AlexNet, on Intel Atom, Xeon,
and Xeon Phi processors, spanning the spectrum from mobile devices to
supercomputers. We also open source our project at
this https URL

本文提出一种同时实现卷积神经网络的规模经济和速度提升的方法，包括一种有效的一般性稀疏 - 稠密矩阵乘法实现以及一种性能模型，可以预测不同层和不同计算机架构的稀疏水平的最佳值，该方法可在包括移动设备和超级计算机在内的各种处理器上实现 3.1-7.3 倍的卷积速度提升。